High Glycemic Variability Research Articles

Glycemic Variability Assessed by Continuous Glucose Monitoring and Short-Term Outcome in Diabetic Patients Undergoing Percutaneous Coronary Intervention: An Observational Pilot Study.

Poor glycemic control is associated with unfavorable outcome in patients undergoing percutaneous coronary intervention (PCI), irrespective of diabetes mellitus. However a complete assessment of glycemic status may not be fully described by glycated hemoglobin or fasting blood glucose levels, whereas daily glycemic fluctuations may influence cardiovascular risk and have even more deleterious effects than sustained hyperglycemia. Thus, this paper investigated the effectiveness of a continuous glucose monitoring (CGM), registering the mean level of glycemic values but also the extent of glucose excursions during coronary revascularization, in detecting periprocedural outcome such as renal or myocardial damage, assessed by serum creatinine, neutrophil gelatinase-associated lipocalin (NGAL), and troponin I levels. High glycemic variability (GV) has been associated with worse postprocedural creatinine and NGAL variations. Moreover, GV, and predominantly hypoglycemic variations, has been observed to increase in patients with periprocedural myocardial infarction. Thus, our study investigated the usefulness of CGM in the setting of PCI where an optimal glycemic control should be achieved in order to prevent complications and improve outcome.

Reporting on Glucose Control Metrics in the Intensive Care Unit.

The 'diabetes of injury' typically associated with critical illness has recently been thoroughly revisited and much better characterised following major therapeutic advances. The occurrence of severe hyperglycaemia, moderate hypoglycaemia or high glycaemic variability has been associated with an increased mortality and rate of complications in large independent cohorts of acutely ill patients. Hence, current guidelines advocate the prevention and avoidance of each of these three dysglycaemic domains, and the use of a common metrics for a quantitative description of dysglycaemic events, such as the proportion of time spent in the target glycaemic range as a unifying variable. Using a common language will help to face the future challenges, including the definition of the most appropriate blood glucose (BG) target according to the category of admission, the time interval from the initial injury and the medical history. The clinical testing of technological improvements in the monitoring systems and the therapeutic algorithms should be assessed using the same metrics.

Premature ejaculation is associated with glycemic control in Type 1 diabetes.

Premature ejaculation (PE) is the most common male sexual dysfunction. Its prevalence in Type 1 diabetes is unknown. The aim of this study was to assess the prevalence of PE in Type 1 diabetes and the influence of glycemic control on ejaculatory function. One hundred Type 1 diabetic male patients (age < 40 years) and 51 age-matched nondiabetic control subjects were evaluated for PE. A subgroup of 30 diabetic patients (20 with PE and 10 without) were also evaluated for blood glucose variability. The presence of PE was assessed with the premature ejaculation diagnostic tool (PEDT) and the self-estimated intravaginal ejaculatory latency time (IELT). Glucose variability was evaluated by continuous glucose monitoring for a 7-day period with a DexCom G4 CGM system: the mean amplitude of glycemic excursions (MAGEs), low (LBGI) and high (HBGI) blood glucose indices, and the standard deviation of blood glucose (BGSD) were calculated. PE prevalence did not differ significantly between the two groups: pathological values of the PEDT score (>8) and IELT score (<1 minute) were recorded in 24 out of 100 diabetic patients (24%) and in 12 out of 51 controls (23.5%). There were significant associations between hemoglobin A1c and the PEDT score (r = 0.27; P = 0.006) and IELT (r = -0.3; P = 0.01). In the subgroup assessed for glucose variability, the PEDT score was associated with LBGI (r = 0.43; P = 0.01), but not with BGSD (r = 0.1, P = 0.6), MAGE (r = -0.1; P = 0.4), or HBGI (r = 0.1; P = 0.6). Our results show a similar prevalence of PE in young male patients with Type 1 diabetes and in the age-matched control population; in diabetic patients with PE, a higher glycemic variability in the hypoglycemic domain is significantly associated with the PEDT score.

High Hemoglobin A1c levels and glycemic variability increase risk of severe hypoglycemia in diabetic hemodialysis patients

While hyperglycemia is central to the pathogenesis and management of diabetes mellitus, hypoglycemia and glucose variability also contribute to outcomes. We previously reported on the relationship of glycemic control to outcomes in a large population of diabetic end-stage renal disease (ESRD) patients. Recognizing that ESRD is a risk factor for severe hypoglycemia, we have now analyzed the association between glycosylated hemoglobin A1c (HgbA1c) levels and glycemic variability in those with hypoglycemia. This is a retrospective study of patients with diabetes enrolled in a large hemodialysis program. Hypoglycemia was identified from hospital discharge diagnostic codes. Glycemic variability was assessed by the standard deviation of HgbA1c and glucose levels over time. Hypoglycemia as a discharge diagnosis was documented in 4.1% of patients. Higher baseline HgbA1c was associated with greater risk for hypoglycemia hospitalization, a finding confirmed by time-lagged HgbA1c levels drawn a quarter earlier. Higher baseline HgbA1c categories were also associated with greater variability in HgbA1c levels during the analysis period. Similarly, greater glucose variability was associated with higher mean glucose levels by trend analysis. High, not low, HgbA1c levels are associated with greater risk of severe hypoglycemia, which may derive from glucose variability in the setting of treatment for hyperglycemia. High HgbA1c and glycemic variability are associated with increased risk of hypoglycemia in individuals with diabetes and ESRD.

Glycemic variability in normal glucose regulation subjects with elevated 1-h postload plasma glucose levels

Subjects with normal glucose regulation (NGR), whose 1-h postload plasma glucose is ≥8.6 mmol/L (155 mg/dL, NGR 1 h ≥ 8.6) during 75-g oral glucose tolerance test (OGTT), have an increased risk of type 2 diabetes and subclinical organ damage. And, the deficiency in islet β cell function is responsible for glycemic disorders. The purpose of this study is to investigate glycemic variability in NGR subjects with elevated 1-h postload plasma glucose levels and its association with islet β cell function. The 29 NGR subjects with 1-h postload plasma glucose ≥8.6 mmol/L (NGR 1 h ≥ 8.6) and 29 age- and sex-matched NGR subjects with 1-h postload plasma glucose <8.6 mmol/L (NGR 1 h < 8.6) were recruited in the study. Insulin sensitivity (Matsuda index, ISI), insulin secretion (insulinogenic index ΔI30/ΔG30), and integrated β cell function measured by the oral disposition index (ΔI30/ΔG30 multiplied by the ISI) were derived from OGTT. All subjects were monitored using the continuous glucose monitoring system for consecutive 72 h. The multiple parameters of glycemic variability included the standard deviation of blood glucose (SDBG), mean blood glucose (MBG), mean of daily differences (MODD), and mean amplitude of glycemic excursions (MAGE). MAGE is considered as a gold standard of glycemic variability. Glycemic variability parameters SDBG, MBG, MODD, and MAGE in NGR 1 h ≥ 8.6 group were higher than those in NGR 1 h < 8.6 group (p < 0.05), and oral disposition index in NGR 1 h ≥ 8.6 group was lower than that in NGR 1 h < 8.6 group (p < 0.05). SDBG, MBG, MODD, MAGE, and 1-h postload plasma glucose all negatively associated with oral disposition index in the separate group (p < 0.05) and in the whole subjects (p < 0.05). After multivariate regression analysis, oral disposition index was the strongest independent contributor to MAGE and 1-h postload plasma glucose in the separate group (p < 0.05) and in the whole subjects (p < 0.05). It is concluded that NGR 1 h ≥ 8.6 group had higher glycemic variability and lower oral disposition index, compared with NGR 1 h < 8.6 group. Increased glycemic variability parameters and elevated 1-h postload plasma glucose consistently associated with declined oral disposition index in subjects from NGR 1 h < 8.6 to NGR 1 h ≥ 8.6 group.

Analysis of Chronic Kidney Disease – Asociated Glycemic Variability in Patients with Type 2 Diabetes Using Continuous Glucose Monitoring System

Abstract Background and Aims. In diabetic patients, chronic kidney disease (CKD) requires special attention due to the multitude of factors that determine glycemic variability. We aimed to assess glycemic variability in patients with CKD and type 2 diabetes mellitus (T2DM) using a continuous glucose monitoring system (CGMS) and identify the predictive value of inter-day and intra-day glycemic variability indices for metabolic imbalance. Material and method. We included 20 diabetic patients (10 CKD patients/10 patients without CKD) and 10 healthy volunteers. Anthropometric parameters, glycated hemoglobin (HbA1c), and glycemic variability indices on CGMS readings were registered. Results. CKD diabetic patients presented significantly higher inter-day and intra-day glycemic variability compared to the diabetic patients without CKD. HbA1c was not significantly different between diabetic subjects with/without CKD. ROC curves indicated that just some CGMS parameters had higher predictive value for metabolic imbalance (HbA1c≥6.5%) but only the percentage of time with glucose values&gt;180 mg/dl (p=0.024) was an independent predictor for HbA1c≥6.5%. Conclusions. Subjects with CKD and T2DM had poor glycemic control and significantly higher glycemic variability comparative to those without CKD, and especially to healthy volunteers. Assessment of glycemic variability indices is more accurate than HbA1c for the quantification of glycemic control in CKD diabetic patients

EComment. Intraoperative glycaemic control in cardiac surgical patients

We read with great interest the article by Song and co-workers [1]. We would like to congratulate the authors on their well-designed study with an important message that intraoperative hyperglycaemia >150 mg/dl and high glycaemic variability were independent risk factors of renal dysfunction after off-pump coronary artery bypass surgery, but would also like to add some comments. Intraoperative hyperglycaemia, which occurs frequently in cardiac surgical patients with and without diabetes, has been shown to be associated with increased morbidity and mortality. These patients are exposed to significant adverse consequences including surgical site infection, neurologic, renal, and cardiac complications as well as longer intensive care unit and hospital stay. In recent years, glycaemic control during coronary artery bypass surgery and all cardiac surgical procedures has been the focus of interest. However, an optimal value for glycaemic control has yet to be fully elucidated. Moreover, there are clear potential adverse consequences of tight glycaemic control such as hypoglycaemia [2]. In cardiac surgical patients, a number of observational studies have specifically investigated the effect of intraoperative glycaemic control on outcomes. These studies have suggested an association between greater glycaemic control and improved outcomes. While prospective randomized trials have been promising, the results have been less robust [2-4]. Successful glycaemic control requires a multidisciplinary approach, which includes representation from nursing, anesthesiology, pharmacy, surgery, and endocrinology. All studies have shown that maintaining serum glucose levels <180 mg/dl reduces morbidity and mortality, while the effects of more aggressive control on clinical outcomes are less clearly defined [2]. Recently, Lazar and colleagues [3] performed a prospective, randomized trial in diabetic patients undergoing coronary artery bypass surgery to determine whether tight glycaemic control (90-120 mg/dl) would result in more optimal clinical outcomes than a more moderate glycaemic control (121-180 mg/dl). In their study, patients with tight glycaemic control had a higher incidence of hypoglycaemic events, but this did not result in any clinical sequelae. Moreover, tight glycaemic control did not result in any significant improvement in clinical outcomes that could not be achieved with more moderate control. In a prospective randomized controlled study, Desai and colleagues [4] demonstrated that maintenance of blood glucose in a liberal range (121-180 mg/dl) after coronary artery bypass surgery led to similar outcomes compared with a strict target range (90-120 mg/dl) and was superior in glucose control and target range management. On the basis of the results of this study, a target blood glucose range of 121-180 mg/dl was recommended for patients after coronary artery bypass surgery, as advocated by the Society of Thoracic Surgeons [2,4]. Although we agree that the optimal range for glycaemic control in cardiac surgical patients is 120-180, we should all remember that the exact value for optimal glycaemic control is still unknown and the subject of numerous studies. Conflict of interest: none declared

A Consensus Perceived Glycemic Variability Metric

Glycemic variability (GV) is an important component of overall glycemic control for patients with diabetes mellitus. Physicians are able to recognize excessive GV from continuous glucose monitoring (CGM) plots; however, there is currently no universally agreed upon GV metric. The objective of this study was to develop a consensus perceived glycemic variability (CPGV) metric that could be routinely applied to CGM data to assess diabetes mellitus control. Twelve physicians actively managing patients with type 1 diabetes mellitus rated a total of 250 24 h CGM plots as exhibiting low, borderline, high, or extremely high GV. Ratings were averaged to obtain a consensus and then input into two machine learning algorithms: multilayer perceptrons (MPs) and support vector machines for regression (SVR). In silica experiments were run using each algorithm with different combinations of 12 descriptive input features. Ten-fold cross validation was used to evaluate the performance of each model. The SVR models approximated the physician consensus ratings of unseen CGM plots better than the MP models. When judged by the root mean square error, the best SVR model performed comparably to individual physicians at matching consensus ratings. When applied to 262 different CGM plots as a screen for excessive GV, this model had accuracy, sensitivity, and specificity of 90.1%, 97.0%, and 74.1%, respectively. It significantly outperformed mean amplitude of glycemic excursion, standard deviation, distance traveled, and excursion frequency. This new CPGV metric could be used as a routine measure of overall glucose control to supplement glycosylated hemoglobin in clinical practice.

Increasing blood glucose variability is a precursor of sepsis and mortality in burned patients.

High glycemic variability, rather than a mean glucose level, is an important factor associated with sepsis and hospital mortality in critically ill patients. In this retrospective study we analyze the blood glucose data of 172 nondiabetic patients 18–60 yrs old with second and third-degree burns of total body surface area greater than 30% and 5%, respectively, admitted to ICU in 2004–2008. The analysis identified significant association of increasing daily glucose excursion (DELTA) accompanied by evident episodes of hyperglycemia (>11 mmol/l) and hypoglycemia (<2.8 mmol/l), with sepsis and forthcoming death, even when the mean daily glucose was within a range of acceptable glycemia. No association was found in sepsis complication and hospital mortality with doses of intravenous insulin and glucose infusion. A strong increase in DELTA before sepsis and death is treated as fluctuation amplification near the onset of dynamical instability.

Usefulness of Acarbose and Dietary Modifications to Limit Glycemic Variability Following Roux-en-Y Gastric Bypass as Assessed by Continuous Glucose Monitoring

About 70% of the patients operated on for a gastric bypass (Roux-en-Y gastric bypass [RYGB]) suffer from dumping syndrome. In these patients, previous studies have demonstrated a high glycemic variability with hypoglycemia and with altered continuous glucose monitoring (CGM) profiles. The aim of this study was to evaluate the effect of treatment with dietary counseling plus acarbose administration on the symptoms and on the characteristics of the CGM profile. Eight consecutive patients with dumping syndrome were given dietary counseling for 6 weeks and also treated with acarbose (50-100 mg three times a day). Their symptoms and the features of the CGM were compared before and after treatment. The symptoms disappeared in seven patients. There was a significant increase in the time to the interstitial glucose (IG) peak and a reduction in the rate of the IG increase after a meal and in the rate of the IG decrease following the peak. The time below 60 mg/dL was significantly decreased, and the minimal IG value was significantly increased. The maximum and mean IG levels and the time above 140 mg/dL were decreased, but not significantly. Six patients spent more than 1% of the time with IG values below 60 mg/dL before treatment, but after treatment this was reduced to one patient. Before treatment only one patient had an IG level neither below 60 or above 140 mg/dL, and after treatment four patients were in this category. Dietary counseling and acarbose treatment eliminated the symptoms and improved the CGM profile of patients suffering from dumping syndrome after RYGB.

Prediction of the Risk to Develop Diabetes-Related Late Complications by Means of the Glucose Pentagon Model: Analysis of Data from the Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study

By taking parameters into account that describe the variability of continuously monitored glucose and long-term metabolic control [hemoglobin A1c (HbA1c)], the glucose pentagon model (GPM) allows characterization of the glucose profile of individual patients with diabetes in a graphical format. A glycemic risk parameter (GRP) derived from this model might allow a better prognosis of the risk to develop diabetes-related complications than the HbA1c. To evaluate this hypothesis, we analyzed a subset of data from the Juvenile Diabetes Research Foundation continuous glucose monitoring (CGM) study. The values of the different parameters that are integrated in the GPM were extracted automatically from CGM profiles registered before and after 6 months by means of the Medtronic CGM system in 108 patients. In these patients, the significant reduction in HbA1c from 7.4% to 7.0% was accompanied by a reduction in glycemia from 164 to 156 mg/dl, standard deviation from 61 to 57 mg/dl, area under the curve >160 mg/dl 29.2 to 23.1, and time per day >160 mg/dl 634 to 576 min. This led to a subsequent reduction in GRP from 3.3 to 2.7; this decrease by 18.2% was significantly larger than that in HbA1c by 8.6% (p < .001). Changes in individual GPMs/GRPs support this observation. They also show the impact of high glycemic variability on GPM/GRP. Our analysis of data of a study with a considerable sample size and study duration showed that the GPM is not only helpful for rapid assessment of individual glycemic profiles and how therapeutic interventions influence these, but also appears to provide a better prognosis of the risk to develop late complications than the HbA1c per se. However, it is also clear that a true validation of such a model requires performance of a long-term study in a large number of patients with diabetes.

Alpha-glucosidase inhibitors 2012 – cardiovascular considerations and trial evaluation

Alpha-glucosidase inhibitors have been available for clinical use for about 20 years. They have shown reasonably good efficacy comparable to other oral blood glucose lowering drugs and in some parts of the world are the most commonly prescribed oral diabetes medication, especially in Asian countries. Unlike as has been observed with some other blood glucose lowering agents, however, no adverse signals of potential cardiovascular harm have emerged in relation to their use. On the contrary, significant beneficial cardiovascular outcome results have been observed in the post-hoc analyses of randomised placebo-controlled trials with the alpha-glucosidase inhibitor acarbose. Targeting mainly postprandial hyperglycaemia, alpha-glucosidase inhibitors favourably affect several cardiovascular risk factors, such as obesity, hypertension and high glycaemic variability with little to no risk for hypoglycaemia. Furthermore, acarbose favourably affects endothelial dysfunction and carotid intima media thickening in humans and, in animal models, improves cardiac interstitial fibrosis and hypertrophy of cardiomyocytes. The ultimate determination of the cardiovascular effects of alpha-glucosidase inhibitors in terms of clinical outcomes awaits the results of ongoing long-term, randomised, placebo-controlled trials.

Benefits of subcutaneous continuous insulin infusion in type 1 diabetic patients with high glycemic variability

Abstract Background Hypoglycemia limits the efficacy of intensive insulin therapy, especially in patients with great glucose variability. The extent to which continuous subcutaneous insulin infusion (CSII) overcomes this limitation is unclear. Our aim was to determine whether CSII is helpful for decreasing glucose variability and hypoglycemia, mainly in patients with the greatest variability. Method Twenty-four patients with type 1 diabetes wore a continuous glucose monitoring system sensor for 3 days before starting therapy with CSII and 6 months later. Glucose variability (SD, MAGE, M) and hypoglycemia duration (area under the curve (AUC) Results At 6 months, a decreased glucose variability was seen, as measured by MAGE, M, and SD (median: −28 mg/dL (interquartile range, −48 to 1), p = 0.03; −22 (−40 to 0), p = 0.04; −11 (−23 to 0), p = 0.009; respectively). Patients with the greatest initial glucose variability (MAGE quartile 4) showed a greater decrease in both MAGE (−47 mg/dL (−103 to −34) vs −20 (−36 to 17), p = 0.01) and AUC Conclusions During CSII, glucose variability significantly decreased, especially in patients with the greatest initial variability. Hypoglycemia was also markedly less in patients with greater variability, with the greatest reduction occurring in those who experienced more marked hypoglycemia with CSII.

Influence of Glycemic Variability on the HbA1c Level in Elderly Male Patients with Type 2 Diabetes

To investigate the influence of glycemic variability on the HbA1c level in elderly male patients with type 2 diabetes (T2DM). The 24-h glucose profiles were obtained using a continuous glucose monitoring system in 291 elderly male type 2 diabetic patients. The relationship between the glycemic variability and HbA1c level was assessed in these patients. The mean amplitude of glycemic excursions (MAGE) in patients with HbA1c ≥7.0% was significantly higher than in patients with HbA1c <7.0% (4.33±1.67 vs. 3.48±1.46 mmol/L, p<0.001). A simple (Pearson's) correlation analysis indicated that the MAGE was significantly correlated with the HbA1c (r=0.229, p<0.001). Compared with the lowest quartile, the highest quartile of the MAGE was associated with a significantly increased risk of having a HbA1c ≥7.0% after multiple adjustments (p (for trend) <0.001). The glycemic variability had a significant influence on the HbA1c level in elderly male patients with T2DM. The present data suggests that patients with higher glycemic variability might have higher HbA1c levels.

Glycemic control in critically ill patients

Hyperglycemia is common in critically ill patients and can be caused by various mechanisms, including nutrition, medications, and insufficient insulin. In the past, hyperglycemia was thought to be an adaptive response to stress, but hyperglycemia is no longer considered a benign condition in patients with critical illnesses. Indeed, hyperglycemia can increase morbidity and mortality in critically ill patients. Correction of hyperglycemia may improve clinical outcomes. To date, a definite answer with regard to glucose management in general intensive care unit patients, including treatment thresholds and glucose target is undetermined. Meta-analyses of randomized controlled trials suggested no survival benefit of tight glycemic control and a significantly increased incidence of hypoglycemia. Studies have shown a J- or U-shaped relationship between average glucose values and mortality; maintaining glucose levels between 100 and 150 mg/dL was likely to be associated with the lowest mortality rates. Recent studies have shown glycemic control < 180 mg/dL is not inferior to near-normal glycemia in critically ill patients and is clearly safer. Glycemic variability is also an important aspect of glucose management in the critically ill patients. Higher glycemic variability may increase the mortality rate, even in patients with the same mean glucose level. Decreasing glucose variability is an important issue for glycemic control in critically ill patients. Continuous measurements with automatic closed-loop systems could be considered to ensure that blood glucose levels are controlled within a specific range and with minimal variability.

Higher glycemic variability in very low birth weight newborns is associated with greater early neonatal mortality

Objective: To determine the association between mean glycemia and its variability with perinatal mortality in preterm newborns hospitalized in an intensive care unit (ICU). Methods: Patients admitted to the ICU within the first 12 hours of life, with birth weight <1500 g, at least three blood glucose measurements/day and lack of insulin treatment were evaluated. Association of mean glycemia and its standard deviation (SD) with death during initial 7 days of life was evaluated. Multivariate logistic regression analysis was performed twice, using continuous glucose concentrations and by means of a quintile-based approach correcting for nonnormal distribution and nonlinear effects. Results: A total of 95 newborns were enrolled. Eleven patients (11.5%) died during the initial 7 days of life, overall mortality equaled 22%. Multivariate analysis showed that 5 minute Apgar score and SD of glucose concentrations were significantly associated with increased mortality in both models. Odds ratios (ORs) equaled 0.44; 95% confidence interval (95% CI) 0.27–0.74 and OR 1.34; 95% CI 1.03–2.03 for the continuous model and 0.50 95% CI 0.34–0.75 and OR 1.82 95% CI 1.07–3.11 for the quintile-based model. In both cases, mean glycemia was removed during the stepwise model-building procedure. Conclusions: Higher glycemic variability may be associated with greater odds of perinatal mortality.

Characterizing Blood Glucose Variability Using New Metrics with Continuous Glucose Monitoring Data

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.

High Glycemic Variability Assessed by Continuous Glucose Monitoring After Surgical Treatment of Obesity by Gastric Bypass

Obesity surgery elicits complex changes in glucose metabolism that are difficult to observe with discontinuous glucose measurements. We aimed to evaluate glucose variability after gastric bypass by continuous glucose monitoring (CGM) in a real-life setting. CGM was performed for 4.2 ± 1.3 days in three groups of 10 subjects each: patients who had undergone gastric bypass and who were referred for postprandial symptoms compatible with mild hypoglycemia, nonoperated diabetes controls, and healthy controls. The maximum interstitial glucose (IG), SD of IG values, and mean amplitude of glucose excursions (MAGE) were significantly higher in operated patients and in diabetes controls than in healthy controls. The time to the postprandial peak IG was significantly shorter in operated patients (42.8 ± 6.0 min) than in diabetes controls (82.2 ± 11.1 min, P = 0.0002), as were the rates of glucose increase to the peak (2.4 ± 1.6 vs. 1.2 ± 0.3 mg/mL/min; P = 0.041). True hypoglycemia (glucose <60 mg/dL) was rare: the symptoms were probably more related to the speed of IG decrease than to the glucose level achieved. Half of the operated patients, mostly those with a diabetes background before surgery, had postprandial glucose concentrations above 200 mg/dL (maximum IG, 306 ± 59 mg/dL), in contrast to the normal glucose concentrations in the fasting state and 2 h postmeal. Glucose variability is exaggerated after gastric bypass, combining unusually high and early hyperglycemic peaks and rapid IG decreases. This might account for postprandial symptoms mimicking hypoglycemia but often seen without true hypoglycemia. Early postprandial hyperglycemia might be underestimated if glucose measurements are done 2 h postmeal.

Relationship Between HbA1c on Target, Risk of Silent Hypoglycemia and Glycemic Variability in Patients with Type 2 Diabetes Mellitus

We aimed to compare time spent at low glucose level (silent hypoglycemia, glucose <3.0 mmol/l) and glycemic variability in patients who reached HbA1c <7.0% with those who did not. In 108 type 2 diabetic patients the interstitial glucose concentration was measured with CGMS (Continuous Glucose Monitoring System) over 72 h. Patients were divided in group 1 with an HbA1c <7.0% (n=63) and group 2 with an HbA1c≥7.0% (n=45). 24% in group 1 experienced silent hypoglycemia vs. 11% in group 2 (n. s.), duration of silent hypoglycemia over 48 h was 27±71 min vs. 7±36 min (n. s.). This was also valid for the subgroups treated with insulin. Patients in group 2 had a significantly higher standard deviation of average glucose (2.3±0.8 vs. 1.3±0.6; p<0.001) and MAGE (mean amplitude of glycemic excursions) (4.8±2.1 vs. 2.6±1.1; p<0.001). Silent hypoglycemia tended to occur more often and to last longer in patients with HbA1c <7%. However, patients with HbA1c >7% had a higher glycemic variability. HbA1c >7% wasn't a reliable indicator of lower risk of hypoglycemia.

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.