Glycemic Variability Indices Research Articles

Association of various glycemic variability indices and vascular outcomes in type-2 diabetes patients

Both blood glucose (BG) level and glycemic variability (GV) significantly associate with diabetes-related complications. However, the criterion standard in GV assessment is absent. We aimed to compare different GV indices in association of vascular outcomes.Ten commonly used GV indices based on self-monitored BG data were calculated, and their associations of vascular outcomes including coronary artery disease (CAD), stroke, and chronic kidney disease (CKD) were compared.In total, 288 type 2 diabetes patients (66.5 ± 11.1 years old) were included in present analysis. Spearman correlation analysis showed that only mean amplitude of glycemic excursions (MAGE) significantly correlated with both estimated glomerular filtration rate and urinary albumin creatinine ratio (P ≤ .03). In Cochran-Armitage trend test, vascular outcomes were significantly associated with the increment of BG risk index and MAGE (P ≤ .03). After adjustment for potential confounders, multiple logistic regression results suggested that BG risk index and MAGE still significantly associated with these three vascular outcomes (P ≤ .01), whereas the other GV indices did not. Receiver operating characteristic curve analysis showed that the abilities of BG risk index and MAGE were similar in identifying CAD, stroke, or CKD.BG risk index and MAGE were better associated with vascular outcomes than other GV indices in type 2 diabetes patients.

GlyCulator2: an update on a web application for calculation of glycemic variability indices.

GlyCulator2: an update on a web application for calculation of glycemic variability indices.

High-normal serum thyrotropin levels and increased glycemic variability in type 2 diabetic patients.

High-normal thyrotropin (TSH) is related to reduced insulin sensitivity and may contribute to glycemic disorders in diabetes. We investigated the relationship between normal serum TSH levels and glycemic variability in euthyroid type 2 diabetic patients. A total of 432 newly diagnosed type 2 diabetic patients with euthyroid function and normal serum TSH levels were recruited between March 2013 and February 2017. Insulin sensitivity was evaluated by the Matsuda index (ISIMatsuda) following a 75-g oral glucose tolerance test. Multiple glycemic variability indices, including the mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and standard deviation of glucose (SD), were calculated from glucose data obtained with a continuous glucose monitoring system. Average glucose accessed by 24-h mean glucose (24-h MG) was also calculated. A normal serum TSH level was positively correlated with MAGE, MODD, SD, and 24-h MG (r = 0.206, 0.178, 0.186, and 0.132, respectively, p < 0.01). After adjusting for somatometric parameters, lipid profiles, ISIMatsuda, and HbA1c via multiple linear regression analysis, mean differences [B(95% CI)] in MAGE, MODD, SD, and 24-h MG between the patients in the lowest and highest quartiles of TSH levels were 0.128(0.031, 0.226), 0.085(0.022, 0.148), 0.039(0.001, 0.078), and 0.002(-0.264, 0.267) mmol/L, respectively. High-normal TSH was independently associated with MAGE, MODD, and SD, but not 24-h MG. High-normal serum TSH is a significant additional risk factor for increased glycemic variability in type 2 diabetic patients.

The different methods of assessing glycemic variability, quality of glycemic control and glycemic risk cannot be interpreted as equivalent in clinical practice

ObjectiveSeveral methods are available to calculate glycemic variability (GV), quality of glycemic control (QGC) and glycemic risk (GR). However, clinicians do not easily interpret these data. This study evaluates whether the results of the different methods can be interpreted as equivalent. MethodsA prospective study was performed including outpatients with DMT2 evaluated at the San Ignacio Hospital and the Colombian Diabetes Association in Bogotá, Colombia. From six-day continuous glucose monitoring data, GV (SD, CV, IQR, MODD, MAGE), QGC (M-value, J-index) and GR (LBGI, HBGI) were calculated. Reference values ​​were generated, classifying the patients according to GV control quartiles (excellent, good, fair or poor). The concordance between the different indices was evaluated. ResultsIn total, 140 patients (68.9 ± 11.2 years) were included. The agreement levels (Kappa) between GV indices were moderate, 0.40 (CI 95%:0.29–0.51), 0.42 (CI 95%:0.31–0.53) and 0.39 (CI 95%:0.28–0.50), for CV versus SD, IQR and CONGA respectively. The levels of agreement between GV and QGC indices were minimal (Kappa CV vs. M-value, 0.15CI 95%:0.046–0.26) and weak between the GV and GR indices (Kappa CVvs.LBGI 0.37CI95%:0.26–0.48). The estimators did not improve significantly when the analysis was performed with linearly weighted or quadratic weighted Kappa. ConclusionsThe present study demonstrates that the concordance between the clinical interpretation of the different GV, QGC and GR indices is poor, suggesting that they cannot be assumed as equivalent, so different indices evaluating different concepts, must be evaluated simultaneously to analyze adequately each patient. New studies are needed to evaluate which of the methods better predicts hypoglycemia and microvascular or macrovascular complications.

A randomized controlled trial to compare the effects of sulphonylurea gliclazide MR (modified release) and the DPP-4 inhibitor vildagliptin on glycemic variability and control measured by continuous glucose monitoring (CGM) in Brazilian women with type 2 diabetes

AimsThis study aims to evaluate whether there is a difference between the effects of vildagliptin and gliclazide MR (modified release) on glycemic variability (GV) in women with type 2 diabetes (T2DM) as evaluated by continuous glucose monitoring (CGM). MethodsAn open-label, randomized study was conducted in T2DM women on steady-dose metformin monotherapy which were treated with 50 mg vildagliptin twice daily or 60–120 mg of gliclazide MR once daily. CGM and GV indices calculation were performed at baseline and after 24 weeks. ResultsIn total, 42 patients (age: 61.9 ± 5.9 years, baseline glycated hemoglobin (HbA1c): 7.3 ± 0.56) were selected and 37 completed the 24-week protocol. Vildagliptin and gliclazide MR reduced GV, as measured by the mean amplitude of glycemic excursions (MAGE, p = 0.007 and 0.034, respectively). The difference between the groups did not reach statistical significance. Vildagliptin also significantly decreased the standard deviation of the mean glucose (SD) and the mean of the daily differences (MODD) (p = 0.007 and 0.030). ConclusionsVildagliptin and gliclazide MR similarly reduced the MAGE in women with T2DM after 24 weeks of treatment. Further studies are required to attest differences between vildagliptin and gliclazide MR regarding glycemic variability.

Effect of the GSTM1 gene deletion on glycemic variability, sympatho-vagal balance and arterial stiffness in patients with metabolic syndrome, but without diabetes

AimsAn increased rate of cerebrovascular complications in patients with metabolic syndrome (MetS) has been reported. Previous studies demonstrated an association between glycemic variability (GV) and cerebrovascular reactivity (CRV) in MetS, thus suggesting a putative role of GV on cerebrovascular events. Although the pathophysiological mechanism linking GV to damage is still to be elucidated, evidence suggests oxidative stress plays a crucial role. Since functional variants in glutathione S-transferases (GST) genes modulate the cellular detoxification processes, the aim of this study was to elucidate the involvement of GSTs in MetS and investigating the correlation with GV, arterial stiffness, and sympatho-vagal (SV) balance. MethodsA hundred metabolic syndrome patients without diabetes underwent GST gene polymorphism analysis and a sub-sample 36 patients were randomly selected to investigate the correlation between GST gene polymorphisms and GV, and sympatho-vagal (SV) balance and arterial stiffness. ResultsGSTM1 showed a significant association with several GV, arterial stiffness, and SV balance indexes. In particular, the GSTM1 deletion positively correlates with lower values of these indexes when compared to the presence of the gene. ConclusionsTherefore, we suggested a global influence of GSTM1 deletion on the GV, arterial stiffness, and SV balance pathways in MetS patients, probably also interacting with AMP-activated protein kinase (AMPK) regulation.Our novel findings indicate GSTM1 could be a risk locus in MetS development and shed light novel scenarios on the role of glucose fluctuations in neurological impairments.

Range of glucose as a glycemic variability and 3-month outcome in diabetic patients with acute ischemic stroke.

Glycemic variability (GV) is reportedly a predictor for poor outcome in various clinical conditions. We aimed to assess whether GV during hospital admission is associated with poor outcomes in patients with acute ischemic stroke (AIS) and diabetes. We prospectively enrolled consecutive patients with AIS from the registry of 6 tertiary hospitals between January 2013 and December 2014. For the GV index, we used a glucose level range that was divided into 4 quartiles. Multivariable binary and ordinal logistic regression analyses were performed to determine the association between GV and the modified Rankin Scale score (3–6) at 3 months. We enrolled 1,504 patients with AIS and diabetes (mean age, 68.1 years; male, 57.2%), of which 35.1% had poor outcomes at 3 months. An increasing glucose range quartile was positively associated with initial neurologic severity and development of hypoglycemia during hospital admission. Multivariable analysis showed that the glucose level range quartile was associated with poor outcomes, even after adjusting for the number of glucose measurement and hypoglycemia (odds ratio [OR] Q2 vs. Q1: 1.50, 95% confidence interval [CI]: 1.02–2.18; OR Q3 vs. Q1: 2.01, 95% CI: 1.34–3.01; OR Q4 vs. Q1: 1.98, 95% CI: 1.22–3.23). These associations remained significant after dichotomization according to glycated hemoglobin levels at admission. An increasing glucose level range as a GV index during hospital admission was associated with poor functional outcomes at 3 months in patients with AIS and diabetes.

Diabetes remission after bariatric surgery is characterized by high glycemic variability and high oxidative stress

Background and aimTo evaluate glycemic variability (GV) and oxidative stress in patients who achieved type 2 diabetes (T2DM) remission after bariatric surgery (BS). Methods and resultsTwenty-two patients (M/F10/12, age 50 ± 9 years, BMI 31 ± 6 kg/m2) who were in remission of T2DM (T2DM remitters) after BS since at least 1 year and 22 age-, sex- and BMI-matched control subjects were studied. Of the BS group, eleven subjects had undergone Roux-en-Y gastric bypass (RYGB) and eleven subjects sleeve gastrectomy (SG). Oral glucose tolerance test (OGTT), 7 days-continuous glucose monitoring, 24-h urinary excretion of 8-isoprostaglandin F2α (8-isoPGF2α) and dietary intake evaluation were performed. According to general linear model for repeated measures, glucose and insulin response during OGTT were significantly different in T2DM remitter than in control subjects (p < 0.001, for both). All measures of GV (standard deviation, coefficient of variation and mean amplitude of glucose excursions) were significantly higher in T2DM remitters than in controls, (p < 0.001 for all). These indexes were higher among RYGB than SG patients (p < 0.05). The time spent out of the 60–160 mg/dl range was significantly longer in T2DM remitters undergoing RYGB than in controls (p < 0.02). Mean 24-h urinary 8-isoPGF2α excretion was significantly higher in T2DM remitters than that of control subjects (p = 0.04). All GV indexes were directly correlated with blood glucose levels at 30 and 60 min during OGTT (p < 0.05–0.001). ConclusionRemission of T2DM after BS is characterized by high GV and high oxidative stress in the face of fasting blood glucose and HbA1c within the normal range.

Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data.

Tens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed. Here, we analyzed the feasibility of using CGM-based GV indices to distinguish healthy from IGT&T2D and IGT from T2D subjects by means of a machine-learning approach. The data set consists of 102 subjects belonging to three different classes: 34 healthy, 39 IGT, and 29 T2D subjects. Each subject was monitored for a few days by a CGM sensor that produced a glucose profile from which we extracted 25 GV indices. We used a two-step binary logistic regression model to classify subjects. The first step distinguishes healthy subjects from IGT&T2D, the second step classifies subjects into either IGT or T2D. Healthy subjects are distinguished from subjects with diabetes (IGT&T2D) with 91.4% accuracy. Subjects are further subdivided into IGT or T2D classes with 79.5% accuracy. Globally, the classification into the three classes shows 86.6% accuracy. Even with a basic classification strategy, CGM-based GV indices show good accuracy in classifying healthy and subjects with diabetes. The classification into IGT or T2D seems, not surprisingly, more critical, but results encourage further investigation of the present research.

Role of Mobile Technology to Improve Diabetes Care in Adults with Type 1 Diabetes: The Remote-T1D Study iBGStar® in Type 1 Diabetes Management.

IntroductionThe role of mobile technology in patient-reported outcomes (PRO) and glycemic control in adults with type 1 diabetes (T1D) needs further evaluation.MethodsThe single-center, prospective, 6-month, open-label, investigator-initiated study randomized 100 subjects with T1D in a 1:1 fashion to a control group using self-monitoring of blood glucose (SMBG) with Accu-Chek Nano® and an intervention group using SMBG with iPhone plus glucose meter (iBGStar®). The primary endpoint was the change in PRO (hypoglycemia fear score, behavior and worry subscores). Secondary outcomes were the improvement in glycemic variability indices and the reduction in A1c values.ResultsBaseline demographics and glycosylated hemoglobin (A1c) values were similar in the two groups. There was a significant decrease in A1c value at 6 months in iBGStar® group compared to the control group (−0.16 vs. −0.51, p = 0.04). The total insulin dose increased significantly in the iBGStar® group at 3 months but did not change at 6 months. The hypoglycemia fear scale (PRO) improved in both groups at 6 months (−1.4 ± 10.0 vs. −3.9 ± 12.5, p = 0.32).ConclusionThe use of iBGStar® resulted in better glycemic control and improvement in some PRO (hypoglycemia fear and behavior scores) compared to the control group at 6 months with no increased risk of hypoglycemia.Clinical trial registration ClinicalTrials.gov: NCT01825382.FundingSanofi.Electronic supplementary materialThe online version of this article (doi:10.1007/s13300-017-0272-5) contains supplementary material, which is available to authorized users.

Glycemic Variability and Its Association With Demographics and Lifestyles in a General Adult Population

Objective:The objective was to investigate glycemic variability indices in relation to demographic factors and common environmental lifestyles in a general adult population.Methods:The A Estrada Glycation and Inflammation Study is a cross-sectional study covering 1516 participants selected by sampling of the population aged 18 years and over. A subsample of 622 individuals participated in the Glycation project, which included continuous glucose monitoring procedures. Five glycemic variability indices were analyzed, that is, SD, MAGE, MAG, CONGA1, and MODD.Results:Participants had a mean age of 48 years, 62% were females, and 12% had been previously diagnosed with diabetes. In the population without diabetes, index distributions were not normal but skewed to the right. Distributional regression models that adjusted for age, gender, BMI, alcohol intake, smoking status, and physical activity confirmed that all indices were positively and independently associated with fasting glucose levels and negatively with heavy drinking. SD, MAGE, and CONGA1 were positively associated with aging, and MAG was negatively associated with BMI. None of the GVI studied were influenced by physical activity. Age-group-specific reference values are given for the indices.Conclusions:This study yielded age-specific reference values for glucose variability indices in a general adult population. Significant increases were observed with aging. Heavy drinking of more than 140 g/week was associated with significant decreases in variability indices. No differences were found between males and females. These normative ranges provide a guide for clinical care, and may offer an alternative treatment target among persons with diabetes.

Measures of Glycemic Variability in Type 1 Diabetes and the Effect of Real-Time Continuous Glucose Monitoring.

To report the impact of continuous glucose monitoring (CGM) on glycemic variability (GV) indices, factors predictive of change, and to correlate variability with conventional markers of glycemia. Data from the JDRF study of CGM in participants with type 1 diabetes were used. Participants were randomized to CGM or self-monitored blood glucose (SMBG). GV indices at baseline, at 26 weeks in both groups, and at 52 weeks in the control group were analyzed. The associations of demographic and clinical factors with change in GV indices from baseline to 26 weeks were evaluated. Baseline data were available for 448 subjects. GV indices were all outside normative ranges (P < 0.001). Intercorrelation between GV indices was common and, apart from coefficient of variation (CV), low blood glucose index (LBGI), and percentage of glycemic risk assessment diabetes equation score attributable to hypoglycemia (%GRADEhypoglycemia), all indices correlate positively with HbA1c. There was strong correlation between time spent in hypoglycemia, and CV, LBGI, and %GRADEhypoglycemia, but not with HbA1c. A significant reduction in all GV indices, except lability index and mean absolute glucose change per unit time (MAG), was demonstrated in the intervention group at 26 weeks compared with the control group. Baseline factors predicting a change in GV with CGM include baseline HbA1c, baseline GV, frequency of daily SMBG, and insulin pump use. CGM reduces most GV indices compared with SMBG in people with type 1 diabetes. The strong correlation between time spent in hypoglycemia and CV, LBGI, and %GRADEhypoglycemia highlights the value of these metrics in assessing hypoglycemia as an adjunct to HbA1c in the overall assessment of glycemia.

Vildagliptin reduces plasma stromal cell-derived factor-1α in patients with type 2 diabetes compared with glimepiride.

Aims/IntroductionDipeptidyl peptidase‐4 inhibitors might have pleiotropic protective effects on cardiovascular disease (CVD), in contrast to sulfonylureas. Therefore, we compared various CVD risk factors between vildagliptin and glimepiride.Materials and MethodsWe carried out a randomized, prospective and crossover trial. A total of 16 patients with type 2 diabetes whose glycated hemoglobin was >7% were randomized to add vildagliptin or glimepiride. After 12‐week treatment, each drug was replaced with the other for another 12 weeks. Before and after each treatment, glucose homeostasis and CVD risk factors were assessed, and the continuous glucose monitoring system was applied to calculate glycemic variability.ResultsThe mean age of the participants was 60 years, 31% were men, body mass index 25.5 kg/m2 and HbA1c 8.41%. Both vildagliptin and glimepiride significantly decreased glycated hemoglobin and glycemic variability indices. Despite the improved glucose homeostasis, favorable change of CVD markers was not prominent in both the arms, along with significant weight gain. Only plasma stromal cell‐derived factor (SDF)‐1α decreased by 30% in the vildagliptin arm. According to regression analyses, the reduction of SDF‐1α was independently associated with vildagliptin usage and serum interleukin‐6 changes, but white blood cells were not related with the SDF‐1α changes.ConclusionCompared with glimepiride, vildagliptin arrestingly decreased plasma SDF‐1α, and its clinical implications should be further investigated.

Study of Glycemic Variability Through Time Series Analyses (Detrended Fluctuation Analysis and Poincaré Plot) in Children and Adolescents with Type 1 Diabetes.

Time series analysis provides information on blood glucose dynamics that is unattainable with conventional glycemic variability (GV) indices. To date, no studies have been published on these parameters in pediatric patients with type 1 diabetes. Our aim is to evaluate the relationship between time series analysis and conventional GV indices, and glycosylated hemoglobin (HbA1c) levels. This is a transversal study of 41 children and adolescents with type 1 diabetes. Glucose monitoring was carried out continuously for 72 h to study the following GV indices: standard deviation (SD) of glucose levels (mg/dL), coefficient of variation (%), interquartile range (IQR; mg/dL), mean amplitude of the largest glycemic excursions (MAGE), and continuous overlapping net glycemic action (CONGA). The time series analysis was conducted by means of detrended fluctuation analysis (DFA) and Poincaré plot. Time series parameters (DFA alpha coefficient and elements of the ellipse of the Poincaré plot) correlated well with the more conventional GV indices. Patients were grouped according to the terciles of these indices, to the terciles of eccentricity (1: 12.56-16.98, 2: 16.99-21.91, 3: 21.92-41.03), and to the value of the DFA alpha coefficient (> or ≤1.5). No differences were observed in the HbA1c of patients grouped by GV index criteria; however, significant differences were found in patients grouped by alpha coefficient and eccentricity, not only in terms of HbA1c, but also in SD glucose, IQR, and CONGA index. The loss of complexity in glycemic homeostasis is accompanied by an increase in variability.

Relationship between indices of daily glycemic variability and long-term glucose fluctuations

Relationship between indices of daily glycemic variability and long-term glucose fluctuations

Glucose Variability in a 26-Week Randomized Comparison of Mealtime Treatment With Rapid-Acting Insulin Versus GLP-1 Agonist in Participants With Type 2 Diabetes at High Cardiovascular Risk.

A1C is associated with diabetes complications but does not reflect glycemic variability (GV), which may worsen outcomes by inducing inflammation, oxidative stress, and cardiac arrhythmias. We tested whether a glucagon-like peptide 1 agonist-based regimen can reduce GV and cardiometabolic risk markers while maintaining similar A1C levels in people with insulin-requiring type 2 diabetes and high cardiovascular risk. After run-in on metformin and basal-bolus insulin (BBI), 102 participants continued metformin and basal insulin and were randomized to exenatide dosing before the two largest meals (glucacon-like peptide-1 receptor agonist and insulin [GLIPULIN group]) or continuation of rapid-acting insulin analogs (BBI group). Indices of GV by continuous glucose monitoring (CGM), hypoglycemia, weight, risk markers, and cardiac arrhythmias were assessed. The primary end point was change in glucose coefficients of variation (CV) by CGM from baseline to 26 weeks. At randomization, the median A1C was 7.3% (57 mmol/mol) for GLIPULIN and 7.4% (56.3 mmol/mol) for BBI, and glucose CVs were 30.3 for BBI and 31.9 for GLIPULIN. At 26 weeks, A1C levels were similar (7.1% [54 mmol/mol] vs. 7.2% [55 mmol/mol]), whereas mean CV improved with GLIPULIN (-2.4 vs. 0.4, P = 0.047). Other GV indices followed similar nonsignificant patterns of improvement with GLIPULIN. There were no differences in hypoglycemic events during CGM or arrhythmias during electrocardiographic monitoring. On-trial changes in body weight (-4.8 kg vs. +0.7 kg, P < 0.001), alanine aminotransferase (P = 0.0002), and serum amyloid A (P = 0.023) favored GLIPULIN. GLIPULIN reduced GV, weight, and some cardiometabolic risk markers while maintaining equivalent A1C levels versus BBI and might improve clinical outcomes in a larger trial.

Glycemic variability is an important risk factor for cardiovascular autonomic neuropathy in newly diagnosed type 2 diabetic patients

BackgroundThe relationship between glycemic variability, another component of glycemic disorders as well as chronic sustained hyperglycemia, and cardiovascular autonomic neuropathy (CAN) has not been clarified. Our aim is to investigate the association between glycemic variability and CAN in newly diagnosed type 2 diabetic patients. MethodsEwing tests were performed in 90 newly diagnosed type 2 diabetic patients and 37 participants with normal glucose tolerance as control from May 1, 2009, through September 30, 2010. According to the scores from Ewing tests, diabetic patients were divided into two groups: without CAN (CAN−) and with CAN (CAN+). All participants underwent a 48-h to 72-h continuous glucose monitoring (CGM). Coefficient of variability of glycemia (%CV), mean amplitude of glycemic excursions (MAGE) and means of daily differences (MODD) were calculated with the CGM data. ResultsThe prevalence of CAN in patients with newly diagnosed type 2 diabetes was 22.2%. An increasing trend of glycemic variability was found from control group, CAN− group to CAN+ group. MAGE in CAN+ group was significantly higher than that in CAN− group (5.27±1.99mmol/L vs. 4.04±1.39mmol/L, P=0.001). In the Logistic regression analysis, a significant relationship was shown between MAGE and CAN [odds ratio (OR): 1.73, 95% confidence interval (CI): 1.01–2.73, P=0.018)]. The area under the receiver-operating characteristic curve for MAGE was superior to those for other dysglycemic indices in detecting CAN. ConclusionsGlycemic variability is associated with CAN in patients with newly diagnosed type 2 diabetes. Among the glycemic variability indices, MAGE is a significant indicator for detecting CAN.

The correlation of dawn phenomenon with glycemic variability parameters in type 2 diabetes mellitus

Abstract Introduction. Dawn phenomenon could have deleterious effect on overall glycemic control. Glycemic variability may be an independent risk factor for the development of diabetes chronic complications. The study aimed to evaluate any correlations between the dawn phenomenon and parameters of glycemic variability in a cohort of type 2 diabetes patients (T2DM). Material and methods. This retrospective observational study included 131 T2DM patients. Continuous glucose monitoring (CGM) has been performed. Data from the first 24h of full recording were used for analysis of glycemic variability indices: mean level of 24h interstitial glucose value and standard deviation; % coefficient of variation; J index; mean amplitude of glycemic excursion - MAGE; continuous overall net glycemic action (CONGA) at 1, 2, 4 and 6 hours; mean of daily differences (MODD) index. Results. Mean age was 56.04 ± 9.91 years, 35.9% women, 17.6% on diet, 53.4% on oral therapy and 29% on insulin. Dawn phenomenon was more frequent in patients below 60 years (70%) and in oral therapy group (72.85%). Significant correlations between the dawn phenomenon and j-index, MAGE, CONGA-4 and CONGA-6 have been found in T2DM patients on diet therapy alone. The amplitude of dawn phenomenon was 46.10 ± 24.40 mg/dl and significantly correlated (p&lt;0.05) after adjustment for age, gender and treatment with % CV, MAGE, CONGA-1, CONGA-2, CONGA-4, CONGA-6 and MODD. Conclusions. The dawn phenomenon significantly increases the glycemic variability parameters in drug-naive T2DM patients, with no impact in T2DM on oral or insulin therapy.

Association of morning fasting blood glucose variability with insulin antibodies and clinical factors in type 1 diabetes.

The fasting blood glucose concentration in type 1 diabetes may vary without being much affected by diet and exercise. This study aimed to identify association of morning fasting blood glucose concentration variability with insulin antibodies and clinical factors. The subjects in this study were 54 patients with type 1 diabetes who had high variation of fasting blood glucose. The insulin antibody level was measured, and correlations of glycemic variability with antibody levels, binding rates, and other clinical factors were investigated. The standard deviation (SD) of the 30-day morning self-monitored fasting blood glucose concentration (FBG SD) was evaluated as an index of glycemic variability. The mean glucose level was 159.8±42.1 mg/dL and the FBG SD was 47.5±22.0 mg/dL. Glycemic variability (FBG SD) was positively correlated with insulin antibody level, but not with insulin antibody binding rate, and had a negative correlation with C-peptide immunoreactivity/plasma glucose (CPR/PG) and positive correlations with diabetes duration, basal insulin dose and bolus insulin dose. Glycemic variability was not correlated with BMI, HbA1c or age. In multiple regression analysis of glycemic variability, CPR/PG was the only significant related factor. The results showed that glycemic variability was mainly influenced by endogenous insulin secretion capacity and was high in patients with high insulin antibody levels. In some patients with a high insulin antibody titer, the antibody may have an effect on the variability of the fasting glucose concentration in type 1 diabetes.

Comparison of glycemic control and variability in patients with type 2 and posttransplantation diabetes mellitus

AimPosttransplantation diabetes mellitus (PTDM) is a common complication after renal transplantation leading to increased cardiovascular morbidity and mortality. In subjects with type 2 diabetes (T2DM) increased glycemic variability and poor glycemic control have been associated with cardiovascular complications. We therefore aimed at determining glycemic variability and glycemic control in subjects with PTDM in comparison to T2DM subjects. MethodsIn this observational study we analyzed 10 transplanted subjects without diabetes (Control), 10 transplanted subjects with PTDM, and 8 non-transplanted T2DM subjects using Continuous Glucose Monitoring (CGM). Several indices of glycemic control quality and variability were computed. ResultsMany indices of both glycemic control quality and variability were different between control and PTDM subjects, with worse values in PTDM. The indices of glycemic control, such as glucose mean, GRADE and M-value, were similar in PTDM and T2DM, but some indices of glycemic variability, that is CONGA, lability index and shape index, showed a markedly higher (i.e., worse) value in T2DM than in PTDM (P value range: 0.001–0.035). ConclusionsAlthough PTDM and T2DM subjects showed similar glycemic control quality, glycemic variability was significantly higher in T2DM. These data underscore potential important pathophysiological differences between T2DM and PTDM indicating that increased glycemic variability may not be a key factor for the excess cardiovascular mortality in patients with PTDM.