Mean Of Daily Differences Research Articles

Glycemic variability in gestational diabetes mellitus and its association with β cell function

Maternal hyperglycemia in gestational diabetes mellitus (GDM), especially hyperglycemic excursions, is associated with increased risks of adverse pregnancy outcomes. Continuous glucose monitoring (CGM) system (CGMS) is better than intermittent self-measurements in detecting detailed glucose profiles on the magnitude and duration of glucose fluctuations. Hyperglycemia resulted from impaired β cell function. This study analyzed the characteristics of glycemic variability in GDM with 24-28 gestational weeks and its association with β cell function. Thirty GDM with 24-28 gestational weeks (GDM group) were included in this study, and 20 normal gestational women (NGW group) and 20 normal glucose regulation non-pregnant women (NGRW group) were set as controls. The three groups were monitored using the CGMS for consecutive 72h. The parameters of glycemic variability included the standard deviation of blood glucose (SDBG), mean of continuous 24-h blood glucose (MBG), mean amplitude of glycemic excursions (MAGEs), and mean of daily differences (MODDs). Homeostasis model assessments were applied to access the insulin resistance (HOMA-IR). The early insulinogenic index (ΔI30/ΔG30) and the area under the curve of insulin (AUCI180) derived from 75-g oral glucose tolerance test were applied to evaluate the early-phase insulin secretion and second-phase insulin secretion, respectively. After CGM, MAGE and MBG value increased progressively from NGRW, NGW to GDM group (p<0.05); MODD and SDBG values of GDM group were all higher than those of NGRW and NGW groups (p<0.05), but there are no differences in MODD and SDBG between NGRW and NGW groups (p>0.05). After comparison of β cell function, ΔI30/ΔG30 decreased progressively from NGRW, NGW to GDM group (p<0.05); HOMA-IR and AUCI180 increased progressively from NGRW, NGW to GDM group (p<0.05). MAGE value was correlated with ΔI30/ΔG30 and HOMA-IR in GDM group (r=-0.78 and 0.65, respectively, p<0.05). Multiple linear stepwise regression analysis showed that ΔI30/ΔG30 and HOMA-IR were the independent factors of MAGE (β=-0.61, 0.34, respectively, p<0.05). Glycemic variability in GDM was higher than in normal pregnant women, and glycemic variability evaluated by MAGE correlates well with impaired early-phase insulin secretion in GDM. Further large-scale studies are needed to formulate treatment strategies to make up for the impaired early-phase insulin secretion and flat glycemic variability, and analyze the association between pregnancy outcomes improvement and glycemic variability remission in GDM.

Characteristics of real-life glucose profiles monitored by continuous glucose monitoring system in persons with normal glucose tolerance

To decipher the characteristics of real-life glucose profiles in normal glucose tolerance (NGT) persons by continuous glucose monitoring system (CGMS). Forty NGT subjects confirmed by oral glucose tolerance test (OGTT) completed a 3-day period of glucose monitoring via CGMS. The values of 24 h mean blood glucose (MBG), standard deviation of MBG (SDBG), mean amplitude of glycemic excursions (MAGE), largest amplitude of glycemic excursions (LAGE) and means of daily differences (MODD) were 6.0 ± 0.7, 0.9 ± 0.1, 1.9 ± 0.8, 2.9 ± 1.4 and 1.1 ± 0.1 mmol/L respectively. Two of them experienced asymptomatic hypoglycemia defined as glucose concentration < 2.8 mmol/L. And 72.5% (29/40) subjects reached glucose concentrations > 7.8 mmol/L for 5.2 ± 4.6 hours. In addition to higher glucose concentration (FPG: 5.0 ± 0.4 vs 4.8 ± 0.3 mmol/L, MBG: 6.4 ± 0.7 vs 5.7 ± 0.5 mmol/L), the subjects with glucose concentrations > 7.8 mmol/L showed more dramatic glucose excursion represented by higher SDBG (1.1 ± 0.3 vs 0.6 ± 0.2 mmol/L), MAGE (2.3 ± 1.1 vs 1.1 ± 0.3 mmol/L), LAGE (3.3 ± 1.2 vs 2.0 ± 1.0 mmol/L) and MODD (1.2 ± 0.4 vs 0.9 ± 0.3 mmol/L) versus those with glucose concentrations within 7.8 mmol/L. CGMS provides more detailed information of real-life glucose profiles in NGT subjects. And 72.5% NGT subjects in the present study spent a considerable amount of time at pre-diabetic or even diabetic glucose levels characterized by more predominant glucose excursion.

Glucose fluctuations in subjects with normal glucose tolerance, impaired glucose regulation and newly diagnosed type 2 diabetes mellitus

Glycemic variability is poorly studied in the nondiabetic individuals and newly diagnosed patients with type 2 diabetes. The aim of the study is to investigate the characteristics of glucose fluctuations in subjects with normal glucose tolerance (NGT), impaired glucose regulation (IGR) and newly diagnosed, drug-naïve type 2 diabetes mellitus (DM-2). This is a cross-sectional study of three groups including 53 subjects with IGR, 56 DM-2 patients and 53 NGT individuals. Monitoring by a continuous glucose monitoring system (CGMS(®) System Gold(™)) was performed for three consecutive days. Mean blood glucose (MBG), standard deviation of MBG (SDBG), largest amplitude of glycemic excursions (LAGE) and mean amplitude of glycemic excursions (MAGE) were calculated to estimate intraday blood glucose variability. Interday variability of glucose was evaluated by absolute means of daily differences (MODD). Postprandial glucose excursion (PPGE) was calculated to assess the influence of meals on glucose fluctuation. Twenty-two percentage of NGT and 33.9% of IGR individuals experienced blood glucose ≥ 11.1 mmol/l; 49.1% of NGT, 50.9% of IGR and 30.8% of DM-2 participants had hypoglycemic episodes (CGM values <3.9 mmol/l). The IGR and DM-2 groups had greater SDBG (P = 0.010 and P < 0.001), LAGE (P = 0.014 and P < 0.001) and MAGE (P = 0.044 and P < 0.001) compared with the NGT group. Significantly greater MODD and PPGEs were found in the DM-2 groups than in the IGR and NGT groups (P < 0.001). The DM-2 patients had higher 72-MBG and glucose levels overnight than the NGT and IGR subjects (P < 0.001). In the patients with diabetes, MAGE was positively associated with MODD (r = 0.558, P < 0.001) and PPGEs (r = 0.738-0.843, P < 0.001). Glucose variability is present to an increasing degree from NGT to IGR and IGR to DM-2. Compared with the NGT individuals, the IGR and DM-2 subjects show more predominant intraday glucose fluctuations. The DM-2 patients demonstrate increased PPGEs, higher glucose levels overnight and greater interday fluctuations.

The Relationship between Glucose Excursion and Cognitive Function in Aged Type 2 Diabetes Patients

ObjectiveEvidence suggests that type 2 diabetes (T2DM) is associated with an increased risk of dementia and that glucose variability is an independent risk factor for diabetic complications. This study investigated the relationship between glucose excursion and cognitive function in aged T2DM patients. MethodsA total of 248 aged T2DM patients wore a continuous glucose monitoring system (CGMS) for 3 days in order to evaluate glucose excursion, including mean amplitude of glycemic excursions (MAGE) and mean of daily difference (MODD). All subjects were evaluated with a number of accepted cognitive function tests, including the mini-mental status examination (MMSE). The relationship between MAGE and MODD and performance on these cognitive tests was assessed. ResultsThe MAGE and MMSE score were negatively correlated, likewise with the correlation between MODD and MMSE. Liner multivariate regression analysis showed that MAGE and MODD were also negatively related to MMSE independent of age, sex, glycemic control, hypertension, smoking, or coronary heart disease history. ConclusionGlucose excursion is related to cognitive function in aged T2DM patients. Elevated glucose excursion decreased the MMSE score, which reflects general cognitive function. Thus, therapy aimed at controlling glucose excursion may be beneficial for maintaining cognitive function in aged T2DM patients.

Comparison Between Sensor-Augmented Insulin Therapy with Continuous Subcutaneous Insulin Infusion or Multiple Daily Injections in Everyday Life: 3-Day Analysis of Glucose Patterns and Sensor Accuracy in Children

Sensor-augmented continuous subcutaneous insulin infusion (CSII) therapy is superior to CSII therapy alone, but little is known on the effectiveness of sensor-augmented multiple daily injections (MDI) therapy. We compared during everyday life mean glucose control and several variability indexes recorded for 3 days by a real-time glucose sensor (Medtronic, Northridge, CA) in two groups of children treated with either CSII or MDI. Fifty-five consecutive subjects were examined: 17 receiving CSII and 38 receiving MDI basal-bolus therapy (age range, 7-22 years). All subjects wore the sensor for 4 days, and 3 days were used for statistical analysis. Mean glucose and SD, coefficient of variation (CV), mean amplitude of glucose excursion (MAGE), mean of daily differences (MODD), continuous overall net glycemic action (CONGA) at 2 and 4 h, blood glucose (BG) rate, area under the curve (AUC) above 180 mg/dL and below 70 mg/dL, Low BG Index (LBGI), and High BG Index (HBGI) were calculated. Patients receiving CSII administered more daily boluses than patients receiving MDI (5.2±1.5 vs. 3.2±0.3, respectively; P=0.001). Mean glucose was lower in the CSII group. AUC above 180 mg/dL and HBGI were higher in the MDI group. CV, CONGA at 2 h, CONGA at 2 h during the day, and HBGI were worse in the MDI group, whereas MODD, LBGI, BG rate, and MAGE were similar. A positive correlation (r=0.95; P<0.05) was found between the paired sensor-meter values. For the glucose values <70 mg/dL, sensitivity was 40%, and specificity was 99%. In our pediatric patients during everyday life sensor-augmented CSII therapy seemed more effective than sensor-augmented MDI therapy, in terms both of glucose mean values and of intraday variability. Mild hypoglycemic episodes and indexes of low BG values were similar in the two groups, although the latter results may be inaccurate because of low sensor sensitivity at low glucose value.

Non-Inferiority of Insulin Glargine Versus Insulin Detemir on Blood Glucose Variability in Type 1 Diabetes Patients: A Multicenter, Randomized, Crossover Study

This study compared the effects of insulin glargine and insulin detemir on blood glucose variability under clinical practice conditions in patients with type 1 diabetes (T1D) using glulisine as the mealtime insulin. This was a multicenter, crossover trial in 88 randomized T1D patients: 54 men and 34 women, 46.8±13.7 years old, with a duration of diabetes of 18±9 years and hemoglobin A1c level of 7.1±0.7%. The per-protocol population included 78 patients: 44 received glargine/detemir and 34 detemir/glargine in the first/second 16-week period, respectively. The primary end point was the coefficient of variation (CV) of fasting blood glucose (FBG). Secondary end points included variability of pre-dinner blood glucose, mean amplitude of glycemic excursions, mean of daily differences, and doses and number of daily insulin injections. The non-inferiority criterion was an insulin glargine/insulin detemir FBG CV ratio with a 95% confidence interval (CI) upper limit ≤1.25. The non-inferiority criterion was satisfied with a mean value of 1.016 (95% CI=0.970-1.065). Intention-to-treat analysis confirmed the non-inferiority with a 95% CI upper limit=1.062. No significant differences were found on secondary objectives, but there was a trend to higher doses and number of daily injections with insulin detemir. A total of eight (four glargine and four detemir) patients reported nine serious adverse events (including one severe episode of hypoglycemia). None of them was considered as related to basal insulins. Serious adverse events led to treatment discontinuation in two patients of the detemir group and none in the glargine group. In T1D patients under clinical practice conditions, insulin glargine was non-inferior to insulin detemir regarding blood glucose variability, as assessed by CV of FBG.

The beneficial effect of α-glucosidase inhibitor on glucose variability compared with sulfonylurea in Taiwanese type 2 diabetic patients inadequately controlled with metformin: preliminary data

Aims Although sulfonylurea added to metformin is the first oral drug combination regimen for patients with type 2 diabetes recommended by the American Diabetes Association/European Association for the Study of Diabetes consensus statement, it does not allow for individualizing and optimizing therapy with respect to sustaining glycemic control and the reduction of glucose variability. We therefore sought to investigate acarbose as an alternative to glibenclamide in combination with metformin and compare the effects on metabolic control and glucose variability. Methods Type 2 diabetic patients 30–70 years of age with glycosylated hemoglobin 7.0%–11.0% while treated with one or two oral antidiabetic drugs were successively enrolled. After 8 weeks of run-in with metformin 500 mg thrice daily, either acarbose 50 mg or glibenclamide 2.5 mg three times daily was randomly added on and force titrated to acarbose 100 mg or glibenclamide 5.0 mg three times daily for the subsequent 16 weeks. Demographic data, biochemical data and continuous glucose monitoring system data were recorded upon randomization and at the end of the study. Various parameters that measure glucose variability were derived from the continuous glucose monitoring system data. Results Of the 51 type 2 diabetes patients enrolled, data from 40 subjects, 20 in each group, were analyzed after excluding those unqualified information. Both drug combinations improved glycemic control. Glucose variability, expressed as mean amplitude of glycemic excursion or continuous overall net glycemic action and mean of daily differences, decreased significantly (all P<.05) after the addition of acarbose but not glibenclamide. The acarbose–metformin combination has the additional benefits of weight reduction and shorter durations of hyperglycemia compared with metformin monotherapy. Conclusions This study suggests that both intraday and interday glucose variability are more effectively reduced by the acarbose–metformin combination than by the glibenclamide–metformin combination, while both combinations reduce the overall glucose level equally.

Continuous glucose monitoring in the patients with diabetic nephropathy

The aim of this study was to compare the difference of blood glucose (BG) fluctuation in the patients of type-2 diabetes mellitus (DM-2) with and without clinical diagnosed diabetic nephropathy (DN) by the continuous glucose monitoring system (CGMS). Thirty DM-2 patients with clinical diagnosed DN and fifteen DM-2 patients without complication underwent continuous glucose monitoring for 3 days (72 h) by CGMS. The difference of daily glucose fluctuation in both groups was compared by the parameter of CGMS. The 24-h mean blood glucose (MBG), minimal BG (MIN-BG), area under curve of BG over 7.8 (AUC7.8), percentage of time of BG over 7.8 (PT7.8), area under curve of BG over 11.1 (AUC11.1), percentage of time of BG over 11.1 (PT11.1), as well as mean of daily difference (MODD) were significantly increased in the group of DN, compared with those in the group of DM-2 without complication (all statistic probability P <0.05). No statistical significance of mean amplitude of glycaemic excursion (MAGE) was found. In the group of DN, MBG, standard deviation of blood glucose (SDBG), large amplitude of glycaemic excursion (LAGE), AUC7.8, PT7.8, AUC11.1, PT11.1, MAGE and MODD were (10.7±1.9)mmol/L, (2.5±1.3)mmol/L, (9.2±3.9)mmol/L, 3.2±1.7, (81±18)%, 1.2±1.0, (42±24)%, (5.8±2.5)mmol/L and (2.6±1.5)mmol/L, respectively. The study showed that the BG level of the patients with DN fluctuated throughout the day. MBG of the patients with DN was higher than that of the patients of DM-2 without complications, with the characteristics of long-lasting high BG period, dramatic instability during the day and especially high postprandial blood glucose. CGMS is a useful tool for physicians to know the details of the change of BG in the patients with DN.

Two-way crossover comparison of insulin glargine and insulin detemir in basal-bolus therapy using continuous glucose monitoring

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.

Normal Reference Range for Mean Tissue Glucose and Glycemic Variability Derived from Continuous Glucose Monitoring for Subjects Without Diabetes in Different Ethnic Groups

Glycemic variability has been proposed as a contributing factor in the development of diabetes complications. Multiple measures exist to calculate the magnitude of glycemic variability, but normative ranges for subjects without diabetes have not been described. For treatment targets and clinical research we present normative ranges for published measures of glycemic variability. Seventy-eight subjects without diabetes having a fasting plasma glucose of <120 mg/dL (6.7 mmol/L) underwent up to 72 h of continuous glucose monitoring (CGM) with a Medtronic Minimed (Northridge, CA) CGMS(®) Gold device. Glycemic variability was calculated using EasyGV(©) software (available free for non-commercial use at www.easygv.co.uk ), a custom program that calculates the SD, M-value, mean amplitude of glycemic excursions (MAGE), average daily risk ratio (ADRR), Lability Index (LI), J-Index, Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), continuous overlapping net glycemic action (CONGA), mean of daily differences (MODD), Glycemic Risk Assessment in Diabetes Equation (GRADE), and mean absolute glucose (MAG). Eight CGM traces were excluded because there were inadequate data. From the remaining 70 traces, normative reference ranges (mean±2 SD) for glycemic variability were calculated: SD, 0-3.0; CONGA, 3.6-5.5; LI, 0.0-4.7; J-Index, 4.7-23.6; LBGI, 0.0-6.9; HBGI, 0.0-7.7; GRADE, 0.0-4.7; MODD, 0.0-3.5; MAGE-CGM, 0.0-2.8; ADDR, 0.0-8.7; M-value, 0.0-12.5; and MAG, 0.5-2.2. We present normative ranges for measures of glycemic variability in adult subjects without diabetes for use in clinical care and academic research.

Reduced Glucose Variability Is Associated With Improved Quality of Glycemic Control in Patients With Type 2 Diabetes: A 12-Month Observational Study

Background: Current diabetes management relies mainly on hemoglobin A1C measurement to assess quality of treatment and to adjust therapy. We assessed the long-term effectivity of therapeutic adjustments in type 2 diabetes with specific focus on indices of glucose variability and multiple criteria for quality of glycemic control. Methods: Continuous glucose monitoring data collected during an observational study involving 405 outpatients with type 2 diabetes were analyzed at baseline and after 12 months. We evaluated the following criteria for glucose variability and quality of glycemic control: mean amplitude of glycemic excursions (MAGE), SD around the mean sensor glucose, mean of daily differences (MODD), fasting and mean sensor glucose, time outside specified glucose ranges, the Glycemic Risk Assessment Diabetes Equation (GRADE) score, High Blood Glucose Index (HBGI), and Low Blood Glucose Index (LBGI). Patients were classified according to quartiles of MAGE. Results: Indices of glucose variability were reduced by 15 to 23% (P 8.9 mmol/l decreased by 57% and 36% (P 8.9 mmol/l (R 2 = 0.374 and 0.877, P < 0.001 for both). Conclusions: Reduction of elevated glucose variability in type 2 diabetic outpatients is associated with lower risk of hypo- and hyperglycemia, and lower A1C values.

Correlation between the Amplitude of Glucose Excursion and the Oxidative/Antioxidative System in Subjects with Different Types of Glucose Regulation

Objective To investigate effects of glucose excursion on the oxidative/antioxidative system in subjects with different types of glucose regulation. Methods A total of 30 individuals with normal glucose regulation (NGR), 27 subjects with impaired glucose regulation (IGR) and 27 subjects with newly diagnosed type 2 diabetes mellitus (T2DM) were selected and recruited for 3 days' continuous glucose monitor system (CGMS) assessment. The data from CGMS was used to calculate the mean amplitude of glycemic excursion (MAGE), mean blood glucose (MBG) and its standard deviation (SDBG), area under the ROC curve when the blood glucose >5.6 mmol/L within 24 h (AUC 5.6), mean of daily differences (MODD), and mean postprandial glucose excursion (MPPGE). In all groups, the content or activity of malondialdehyde (MDA), total antioxidation capacity (TAOC) and glutathione peroxidase (GSH-Px) were detected. Results Glucose excursion parameters of subjects with T2DM or IGR were higher than those of NGR subjects ( P<0.05 or 0.01). Moreover, Glucose excursion parameters of T2DM subjects were higher than those of IGR subjects ( P<0.05 or 0.01). Subjects with T2DM or IGR had significant higher MDA levels and lower GSH-Px/MDA and TAOC/MDA levels compared to NGR subjects ( P<0.01). T2DM subjects had even higher MDA levels and lower GSH-Px/MDA levels than IGR ( P<0.05 or 0.01). According to the median of normal population for MAGE, T2DM and IGR subjects were divided into MAGE>2.6mmol/L Group and MAGE ≤ 2.6mmol/L Group. MAGE>2.6mmol/L Group had higher levels of MDA and lower levels of GSH-Px/MDA than MAGE ≤ 2.6mmol/L Group ( P<0.05). There was no significant difference between the two groups ( P>0.05) in terms of the levels of TAOC/MDA. Pearson correlation analysis showed that MDA was positively correlated with FPG, 2hPG, MAGE, and SBP. GSH-Px/MDA was negatively correlated with MAGE and TC. TAOC/MDA was negatively correlated with FPG. Partial correlation analysis showed that the relationship between MDA and MAGE, GSH-Px/MDA, and MAGE remained significant after adjustments for the other differences among groups. Conclusion Glucose excursion contributed significantly to promoting lipid peroxidation and decreasing antioxidation capacity than chronic sustained hyperglycemia did in the subjects with different types of glucose regulation.

The Use of a Computer Program to Calculate the Mean Amplitude of Glycemic Excursions

The mean amplitude of glycemic excursions (MAGE), traditionally estimated with a graphical approach, is often used to characterize glycemic variability. Here, we tested a proposed software program for calculating MAGE. Development and testing of the software was based on retrospective analyses of 72-h continuous glucose monitoring profile data collected during two different clinical studies involving 474 outpatients (458 with type 2 and 16 with type 1 diabetes) in three cohorts (two type 2 diabetes and one type 1 diabetes), using the CGMS® Gold™ (Medtronic MiniMed, Northridge, CA). Correlation analyses and a Bland-Altman procedure were used to compare the results of MAGE calculations performed using the developed computer program (MAGE(C)) and the original method (MAGE(O)). Close linear correlations between MAGE(C) and MAGE(O) were documented in the two type 2 and the type 1 diabetes cohorts (r = 0.954, 0.962, and 0.951, respectively; P < 0.00001 for all), as was the absence of any systematic error between the two calculation methods. Comparison of the two indices revealed no within-group differences but did show differences among the various antihyperglycemic treatments (P < 0.0001). In each of the study cohorts, MAGE(C) correlated strongly with the SD (r = 0.914-0.943), moderately with the mean of daily differences (r = 0.688-0.757), and weakly with glycosylated hemoglobin A1c and mean sensor glucose (r= 0.285 and r = 0.473, respectively). The proposed computerized calculation of MAGE is a practicable method that may provide an efficient tool for assessing glycemic variability.

Glycemic Variability in Inadequately Controlled Type 1 Diabetes and Type 2 Diabetes on Intensive Insulin Therapy: A Cross-Sectional, Observational Study

Glycemic variability is suggested to be a predictor for the risk of complications of diabetes. A multitude of parameters to express glycemic variability have been described, but no gold standard exists. The easy measurable parameter SD has been shown to be strongly related to other parameters in a group of patients with mostly well-controlled type 1 and type 2 diabetes mellitus (T1DM and T2DM, respectively). Glycemic variability is higher in T1DM compared with T2DM in mixed populations with different treatments, but studies in patients on intensive insulin treatment are lacking. Therefore in this study we investigate different parameters of glycemic variability and differences between T1DM and T2DM in inadequately controlled patients on intensive insulin treatment. In this cross-sectional, observational study we describe glycemic variability, measured as SD, coefficient of variation, continuous overall net glycemic action, and mean of daily differences in a cohort of inadequately controlled T1DM (n = 166) and T2DM (n = 58) patients on intensive insulin treatment. SD of 48 h (SD(total)) was highly correlated to all other measured parameters of glycemic variability (r = 0.66-0.88). All parameters of glycemic variability were significantly higher in T1DM compared to T2DM (P < 0.001), although hemoglobin A1c and mean glucose were comparable and treatment regimen was the same. In the cohort of T2DM patients but not T1DM, a longer duration of insulin therapy was associated with higher glycemic variability. SD(total) is a conveniently measurable parameter to express glycemic variability in patients with inadequate control with intensive insulin therapy. Patients with T1DM and long-lasting T2DM have the highest glycemic variability.

Correlation between glucose fluctuations and carotid intima-media thickness in type 2 diabetes

Aims To evaluate the relationship between blood glucose fluctuations and carotid atherosclerosis (AS) in type 2 diabetic patients. Methods A case-control study included 36 patients with type 2 diabetes and 10 controls. According to the levels of carotid intima-media thickness (CIMT), 36 diabetic patients were classified into two groups, the diabetes mellitus without atherosclerosis (DM − AS, n = 20) and diabetes mellitus with atherosclerosis (DM + AS, n = 16). The glucose excursions were assessed by the following parameters obtained from continuous glucose monitoring system for 72 h: the mean blood glucose (MBG) and its standard deviation (SD), the area under the ROC curve when the blood glucose was higher than 7.8 mmol/L (AUC7.8), the mean amplitude of glycemic excursion (MAGE), the mean of daily differences (MODD), and the largest amplitude of glycemic excursion (LAGE). Results The levels of MBG, SD, MAGE, LAGE, and AUC7.8 were gradually increased with the progression of atherosclerosis ( P < 0.05). The Spearman's correlation analysis showed that the CIMT was correlated to the age ( R = 0.58, P < 0.001), the duration ( R = 0.50, P < 0.001), the MAGE ( R = 0.34, P = 0.021), and the LAGE ( R = 0.31, P = 0.035). Conclusion These results suggest that glucose fluctuations may accelerate atherogenesis in older type 2 diabetic patients who had a longer duration.

Glipizide controlled‐release tablets, with or without acarbose, improve glycaemic variability in newly diagnosed Type 2 diabetes

1. The aim of the present study was to compare the effects of glipizide controlled-release (CR) tablets monotherapy with that of glipizide CR tablets plus acarbose on glycaemic variability in newly diagnosed Type 2 diabetes (T2DM) patients using a continuous glucose-monitoring system (CGMS). 2. Forty newly diagnosed T2DM patients whose glycated haemoglobin A1c (HbA1c) levels ranged from 7.0% to 9.8% were randomized to either monotherapy or combination therapy. Overall glycaemic control and blood glucose variability were evaluated by CGMS parameters. 3. After 8 weeks treatment, fasting and postprandial blood glucose, HbA1c, glycated albumin (GA), mean blood glucose (MBG), mean amplitude of glycaemic excursions (MAGE), postprandial incremental area under the curve (AUC(pp)) and homeostasis model assessment of insulin resistance decreased significantly in both groups (P < 0.01). There was also a significant decrease in the mean of daily differences (MODD) in the combination therapy group. Mean changes in MBG, MAGE, MODD and AUC(pp) were significantly greater in the combination therapy group than in the monotherapy group (all P < 0.01), whereas no significant differences were found in the mean changes of HbA1c and GA. Multivariate regression analysis showed that the decrement in AUC(pp) was significantly associated with decreases in MAGE. 4. In conclusion, glipizide CR tablets alone or in combination with acarbose can improve overall blood glucose levels and glycaemic variability. Combination therapy using glipizide CR tablets and acarbose was more effective in reducing intraday and day-to-day glycaemic variability than glipizide CR tablet monotherapy.

Responses to Continuous Glucose Monitoring in Subjects with Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion or Multiple Daily Injections

We compared changes in response to unmasking of continuous glucose monitoring (CGM) in subjects with type 1 diabetes who use multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII). Use of real-time CGM (DexCom [San Diego, CA] SEVEN was studied in 38 subjects using CSII and 26 using MDI. CGM output was masked during Week 1 and unmasked during Weeks 2 and 3. We evaluated changes in 16 criteria for quality of glycemic control and eight criteria for glycemic variability. All 24 criteria showed highly statistically significant improvement when considered simultaneously (P < 0.000001). For subjects using CSII, 18 of 24 criteria improved significantly (nominal P < 0.05); for subjects using MDI, 16 of 24 criteria improved significantly (P < 0.05). Twelve of the comparisons remained significant (P < 0.05) after applying the overconservative Bonferroni correction for multiple comparisons. The percentage of glucose values within the range 80-140 mg/dL increased by 19% and 17% relative to their control values (Week 1) for subjects using MDI and CSII, respectively. Mean glucose, overall SD (SD(T)), SD between daily means (SD(dm)), mean amplitude of glycemic excursion (MAGE), and mean of daily differences (MODD) improved significantly. Responses to CGM display were not significantly different between the MDI and CSII subject groups for any of the 24 criteria considered individually or in groups of eight, 16, or 24. CGM has similar effectiveness in subjects with type 1 diabetes using either CSII or MDI.

Improved Quality of Glycemic Control and Reduced Glycemic Variability with Use of Continuous Glucose Monitoring

We evaluated effects of unmasking of continuous display of continuous glucose monitoring (CGM) on quality of glycemic control and glycemic variability. We reanalyzed CGM data from 85 patients using a 7-day glucose sensor. Glucose values were "masked" during the first week but "unmasked" during the next 2 weeks. We evaluated 48 criteria for quality of glycemic control, including mean glucose, SD, percentage of values within-, above- or below- specified ranges, Schlichtkrull's M(100) index, mean amplitude of glycemic excursion (MAGE), mean of daily differences (MODD), the J index, "Index of Glycemic Control" (IGC), Hyperglycemia Index, Hypoglycemia Index, High Blood Glucose Index (HBGI), Low Blood Glucose Index (LBGI), average daily risk range (ADRR), GRADE scores, and CONGA(n). We calculated SD values between daily means, between days-within time points, within days, between time points (for the average glucose profile for several days), and within series for time segments of arbitrary length. Unmasking CGM displays resulted in rapid, highly statistically significant improvement in 29 indices, including percentage within, percentage above, and percentage below target range, mean glucose, SD, SD of daily means, MODD, M(100), IGC, GRADE, HBGI, and J index. Both hyperglycemia and hypoglycemia improved during the first week after unmasking; further improvement in hypoglycemia was seen during the following week. Results obtained using multiple criteria were consistent and highly correlated. Continuous access to display of CGM sensors dramatically improved 29 indices of glycemic control and glycemic variability.

New and Improved Methods to Characterize Glycemic Variability Using Continuous Glucose Monitoring

Glycemic variability is a possible risk factor for development of complications from diabetes. Numerous methods have been used to characterize glycemic variability. We propose several new methods to characterize glycemic variability. We evaluated these methods empirically and theoretically and compared them with previous methods. We describe (1) extension and generalization of the mean amplitude of glycemic excursion (MAGE), i.e., "within day variability," (2) extension and generalization of the mean of daily differences (MODD), i.e., the "between day-within time points variability," (3) "between daily means variability," (4) "between time points variability" of the glucose profile averaged over several days, (5) "within series variability" for a time segment of any arbitrary length, (6) new measures of the stability of the daily glycemic patterns, (7) new types of graphical displays, including within day variability, between day-within time points variability, and between daily means variability versus total variability, and between daily means variability versus within day variability, and (8) new methods to evaluate whether within series and between day-within time points variability fluctuate systematically by time of day. We examined the new measures in relation to previous measures of glycemic variability using correlation analysis on a clinical dataset for 85 subjects. MAGE, MODD, and continuous overall net glycemic action (CONGA(n)) are directly proportional to total standard deviation (SD). MAGE is highly correlated with both total SD and within day variability but weakly correlated with measures of between day variability. MODD is highly correlated with between day-within time points variability and total SD but weakly correlated with measures of within day variability. We provide a systematic, logical framework to characterize multiple aspects of glycemic variability and have implemented a simple, practical computing format. This approach can help clinical researchers and clinicians identify the major sources of variability for any given patient and monitor responses to interventions.

Interpretation of Continuous Glucose Monitoring Data: Glycemic Variability and Quality of Glycemic Control

There are a large number of measures of glycemic variability, including standard deviation (SD), percentage coefficient of variation (%CV), interquartile range (IQR), mean amplitude of glucose excursion (MAGE), mean of daily differences (MODD), and continuous overlapping net glycemic action over an n-hour period (CONGA(n)). These are all highly correlated with the overall or "total" SD, SD(T). SD(T) is composed of several components corresponding to within-day variability, between-day variability (between daily means and between days-within specified time points), and the interaction of these sources of variability. We identify several subtypes of SD; each is highly correlated with SD(T). Variability may also depend on time of day. Numerous measures of quality of glycemic control have been proposed, including a weighted average of glucose values (M)(e.g., M(100) is M at 100 mg/dL), a measure of quality of glycemic control based on mean and SD (J), the Glycemic Risk Assessment Diabetes Equation (GRADE), the Index of Glycemic Control (IGC), the High Blood Glucose Index (HBGI), the Low Blood Glucose Index (LBGI), the Average Daily Risk Range (ADRR), and percentage of glucose values within specified ranges. These methods usually but not always give consistent results: they can differ widely in terms of their ability to detect responses to therapeutic interventions. Based on review of the advantages and limitations of these measures and on extensive experience in the application of these methods, we outline a systematic approach to the interpretation of continuous glucose monitoring data for use by clinical researchers and clinicians to evaluate the quality of glycemic control, glucose variability including within- and between-day variability, the day-to-day stability of glycemic patterns, and changes in response to therapy.