Measures Of Glucose Variability Research Articles

Erratum to “How well do glucose variability measures predict patient glycaemic outcomes during treatment intensification in type 2 diabetes?” [Diabetes Res. Clin. Pract. 108 (2015) 179–186

Erratum to “How well do glucose variability measures predict patient glycaemic outcomes during treatment intensification in type 2 diabetes?” [Diabetes Res. Clin. Pract. 108 (2015) 179–186

Randomized clinical trial of the efficacy and safety of insulin glargine vs. NPH insulin as basal insulin for the treatment of glucocorticoid induced hyperglycemia using continuous glucose monitoring in hospitalized patients with type 2 diabetes and respiratory disease

AimsTo investigate the clinical efficacy and safety of insulin glargine compared with NPH insulin as basal insulin for the management of corticosteroid-induced hyperglycemia in hospitalized people with type 2 diabetes (T2DM) and respiratory disease. Materials and methodsRandomized, two-arm parallel group, clinical trial undertaken from February 2011 to November 2012 on the pneumology ward of the Hospital Regional Universitario de Málaga (Spain), involving 53 participants with T2DM treated with medium/high doses of intermediate-acting corticosteroids. Participants were randomly assigned to receive one single dose of insulin glargine or NPH insulin in three equally divided doses before each meal as basal insulin within a basal–bolus insulin protocol. The intervention lasted six days or until discharge if earlier. ResultsNo significant differences were seen between groups during the study in mean blood glucose (11.43±3.44mmol/l in glargine vs. 11.88±2.94mmol/l in NPH, p=0.624), and measures of glucose variability (standard deviation 3.27±1.16mmol/l vs. 3.61±0.99mmol/l, p=0.273; coefficient of variation 1.55±0.33mmol/l vs. 1.72±0.39mmol/l, p=0.200). Results from CGM were concordant with those obtained with capillary blood glucose reading. The length of hospital stay was also similar between groups (8.2±2.8 days vs. 9.8±3.4 days, p=0.166) There was a non significant trend for lower episodes of mild (4 vs. 8, p=0.351) and severe hypoglycemia (0 vs. 3, p=0.13) in the glargine group. ConclusionsThe results of this study showed that insulin glargine and NPH insulin are equally effective in a basal–bolus insulin protocol to treat glucocorticoid-induced hyperglycemia in people with T2DM on a pneumology ward.

How well do glucose variability measures predict patient glycaemic outcomes during treatment intensification in type 2 diabetes?

AimDespite links to clinical outcomes in patients with type 2 diabetes mellitus (T2DM), the clinical utility of glycaemic variability (GV) measures is unknown. We evaluated the correlation between baseline GV, and glycated haemoglobin (HbA1c) attainment and hypoglycaemic events during treatment intensification in a large group of patients. MethodsPatient-level data from six 24-week clinical trials of T2DM patients undergoing treatment intensification with basal insulin or comparators (N=1699) were pooled. Baseline GV measures included standard deviation (SD), mean amplitude of glycaemic excursions (MAGE), mean absolute glucose (MAG), coefficient of variation (CV), high blood glucose index (HBGI), and low blood glucose index (LBGI) were correlated with HbA1c change and hypoglycaemic events. ResultsAll mean GV measures, excluding CV which worsened, improved significantly from baseline to Week 24, with the largest proportional reduction obtained for HBGI (−65.5%). When assessed as mean individual percentage changes only HBGI improved significantly. Baseline GV correlated positively with Week 24 HbA1c for SD, MAGE, and HBGI. Baseline HBGI and CV correlated negatively and positively, respectively, with Week 24 HbA1c change. Correlations also existed between most baseline GV measures and age, body mass index, Week 24 fasting plasma glucose, Week 24 postprandial plasma glucose, and hypoglycaemic events; statistical significance depended on the specific measure. ConclusionsPre-treatment GV is associated with glycaemic outcomes in T2DM patients undergoing treatment intensification over 24 weeks. HBGI might be the most robust predictor, warranting validation in dedicated prospective studies or randomized trials to assess the predictive value of measuring GV.

The Association of Glucose Variability and Home Discharge Among Survivors of Critical Illness Managed With a Computerized Decision-Support Tool for Glycemic Control.

In-intensive care unit (ICU) glucose variability (GV) is associated with increased mortality. However, the impact of GV on hospital survivors' morbidity and associated changes in destination at time of hospital discharge are unknown. We studied a retrospective patient cohort in a medical/surgical ICU, requiring insulin infusion, using computer-guided insulin dosing software. Standard deviation (GluSD) and coefficient of variation (GluCV) were used as GV measures. We examined rates of home discharge (H) in the whole cohort and selected subgroups across GV quartiles, between patients with and without H, determinants of H, and determinants of GV and its association with patients' ICU length of stay (LOS). A total of 351 patients met study criteria. The association of GV and H varied among examined subgroups. H increased with GV quartile (GluSD; P = .004). GV was higher in patients with H than non-H (GluSD 36.1 vs 30.0 mg/dl, respectively; P = .002). Increased GV was not a predictor of reduced H on multivariate analysis. GV was inversely associated with patients' ICU LOS in all examined subgroups. Increased number of hypoglycemic events and time to attain target glycemia were independent predictors of reduced H. GV was not associated with adverse impact on H in the present cohort, and its prognostic impact should be considered in the context of ICU LOS of examined patient populations. Further studies are needed to examine the morbidity effects of GV and other glycemia-related measures among hospital survivors of critical illness across varying ICU populations, glycemic control approaches, and glycemic targets.

Glucose Variability: Where It Is Important and How to Measure It

Glucose variability predicts hypoglycemia in both type 1 and type 2 diabetes and has consistently been related to mortality in nondiabetic patients in the intensive care unit. SD and mean amplitude of glycemic excursions have historically been very popular measures of glucose variability. For reasons outlined in this counterpoint, I propose to use coefficient of variation and the mean absolute glucose change as preferred measures of glucose variability.

Glycemic Variability Measures in a Group of Subjects with Type 1 Diabetes and Repeated Severe and Non-Severe Hypoglycemia

There is some evidence that not only hemoglobin A1c (HbA1c) but also short-term variations in blood glucose could represent an independent risk factor for hypoglycemia.1 Normative ranges for measures of glycemic variability (GV) in normal subjects and in continuous glucose monitoring (CGM) data sets obtained from subjects with type 1 diabetes mellitus (T1DM) have been published.2 We sought to describe GV in T1DM patients with repeated episodes of severe hypoglycemia and non-severe hypoglycemia (SH/NSH). Thirty-six T1DM subjects underwent CGM for up to 72 h. Twenty-five of 36 were considered the hypoglycemic group [(H-group), presenting repeated SH/NSH], while the remaining 11 were evaluated as the control group (C-group). General characteristics, biochemical measurements, the number of hypoglycemic episodes, and the awareness of hypoglycemia (Clarke Sign Test) were evaluated. Glycemic variability was calculated using EasyGV© (by N. R. Hill, University of Oxford, Oxford, UK; available at www.easygv.co.uk) for the following: standard deviation (SD), M-value (mg glucose per kg body weight per min), mean amplitude of glucose excursions (MAGE), average daily risk ratio, lability index, J-Index, low blood glucose index (LBGI), high blood glucose index, continuous overlapping net glycemic action, mean of daily differences, glycemic risk assessment diabetes equation (GRADE), and mean absolute glucose. No differences were found in the entire set of clinical data or in HbA1c (H-group: age 34.5 ± 7.8 years, T1DM duration 16.0 ± 6.3 years, HbA1c 6.6 ± .0 %). Scores for the Clarke Sign Test differed for both groups, with higher values in the H-group. The H-group showed higher percentages of values and areas under the curve (AUC) <70 mg/dl with respect to the C-group (11.3 ± 8.4 vs 5.3 ± 5.3% <70 mg/dl; p < .05 and 2.5 ± 1.8 vs 0.8 ± 1.0 mg/dl AUC <70mg/dl; p < .005 for the H-group and the C-group, respectively). The H-group presented significantly higher scores than the T1DM C-group in LBGI (9.3 ± 5.0 vs 4.3 ± 2.4), GRADE (8.0 ± 3.4 vs 4.8 ± 3.5), GRADE-hypoglycemia (GRADE-hypo) (22.8 ± 22.0 vs 9.2 ± 8.9), and M-value (17.7 ± 6.5 vs 10.3 ± 6.0). Using CGM glucose profiles, we have shown that only the variability measures representing increased glycemic risk related to hypoglycemia and the quality of glycemic control differ significantly in T1DM subjects who are prone to repeated episodes of hypoglycemia. Almost all measurements (EasyGV software) from our entire group of subjects with T1DM were above the mean + 2 SD normative values that are described in a nondiabetic population. We could not find differences in the majority of measures, except for four (LBGI, GRADE, GRADE-hypo, and M-value), in our group of patients with repeated hypoglycemic episodes in comparison with the control group of T1DM patients without significant hypoglycemia and with good metabolic control. We found differences only in those glucose measures that mainly focused on increased glycemic risk coming from hypoglycemic episodes (LBGI and GRADE-hypo) and those mostly representing quality of glycemic control (GRADE and M-value). The only exception for a glucose measure specifically related to variability was MAGE, which was close to significantly higher in the group of patients with repeated hypoglycemia. As it has been claimed using self-monitoring blood glucose information,3 our data suggest that measures of glucose variability and quality of glucose control derived from CGM data could be used as instruments to estimate the risk of hypoglycemia and could help to prevent it.

Response to Comment on: Rizzo et al. Reduction of Oxidative Stress and Inflammation by Blunting Daily Acute Glucose Fluctuations in Patients With Type 2 Diabetes: Role of Dipeptidyl Peptidase-IV Inhibition. Diabetes Care 2012;35:2076–2082

DeVries (1) was surprised that vildagliptin and sitagliptin groups reported different mean amplitude of glycemic excursions (MAGE) reduction because they have similar fasting and postprandial glucose levels and suggested reporting other measures of glucose variability to better understand these results. Indeed, a previous study evaluating the role of glycemic variability in type 2 diabetic patients has demonstrated that patients with similar mean glucose and postprandial glucose have a markedly different daily glucose profile with differences both in number and duration of excursions (2). The difference in MAGE reduction found in the two study groups …

5thAnnual Symposium on Self-Monitoring of Blood Glucose (SMBG) Applications and Beyond, May 3–5, 2012, Dublin, Ireland

International experts in the fields of diabetes, diabetes technology, endocrinology, and pediatrics gathered for the 5th Annual Symposium on Self-Monitoring of Blood Glucose (SMBG) Applications and Beyond. The aim of this meeting was to continue setting up a global network of experts in this field and provide an international platform for exchange of ideas to improve life for people with diabetes. In line with previous editions, the 2012 meeting comprised a comprehensive scientific program, parallel interactive workshops, pros and cons debates, and a keynote. All these discussions were intended to help identifying gaps and areas where further scientific work and clinical studies are needed.

The association of mean glucose level and glucose variability with intensive care unit mortality in patients with severe acute pancreatitis

PurposeThe objective of this study was to retrospectively analyze the association of mean glucose level (MGL) and glycemic lability index (GLI; as a measure of glucose variability) with intensive care unit (ICU) mortality in patients with severe acute pancreatitis (SAP). Materials and MethodsPaper-based medical records of patients with SAP who were admitted to the ICU of West China Hospital between July 1, 2005, and July 1, 2010, were analyzed. Glucose measurements, demographic characteristics, clinical features, data on the first and second 24-hour Acute Physiology and Chronic Health Evaluation (APACHE) II scores, and outcomes were obtained. Time-weighted glucose parameters were used. We statistically analyzed the relationship between these variables and both ICU and hospital mortality. ResultsA total of 294 patients with 34 796 glucose measurements were included in the final analysis. The time-weighted MGL was 9.31 ± 1.91 mmol/L, and the median of GLI was 55.27 (mmol/L)2 h−1 wk−1. Intensive care unit mortality was 43.5% and increased progressively as GLI increased, reaching 62.5% of patients with GLI above 115.89 (mmol/L)2 h−1 wk−1. The highest odds ratio for ICU death was found in patients with the highest quartile of GLI: odds ratio, 3.47 (95% confidence interval, 1.76-6.86; P < .000). No such relationship could be found with MGL. Glycemic lability index was better able to predict ICU death than was MGL (the area under the curves were 0.642 vs 0.561, respectively; z test was 2.677; P = .0074). The logistic regression analysis showed that GLI, the second 24-hour APACHE II score, and the number of organ failures upon ICU admission contributed independently to the risk of mortality. ConclusionsWe observed that GLI was a better predictor of ICU and hospital mortality than was MGL. Together with the second 24-hour APACHE II score and the number of organ failures upon ICU admission, GLI is an independent predictor of mortality in patients with SAP.

Dynamic Stress Factor (DySF): A Significant Predictor of Severe Hypoglycemic Events in Children with Type 1 Diabetes

Hemoglobin A1c (HbA1c) is the current standard used in the clinical treatment of patients with diabetes. However, it has been shown that patients with similar HbA1c values may have widely different fluctuations in blood glucose values over the same period of time, including time spent in hyper- and/or hypo-glycemia. Hence, there exists a need for quantitative measures that can supplement HbA1c in managing patients with diabetes. We introduce and compare the Dynamic Stress Factor, DySF, a newly developed metric that quantifies glycemic volatility based on patient-specific glucose transition density profiles with HbA1c and with currently used glucose variability metrics in predicting severe hypoglycemia in children with type 1 diabetes. DySF, the daily weighted number of large monotonic glycemic transitions that occur within one hour, was calculated for 441 total subjects with type 1 diabetes (146 children, aged 8-14 yrs) to assess the magnitude and frequency of glucose transitions per day. Severe hypoglycemic episodes (HE) were quantified for all subjects and evaluated against HbA1c and existing measures of glucose variability, including SD, MAGE, MODD, and CONGA using logistic regression models. DySF was found to be a predictor of severe HE in children (p = 0.018) with the likelihood of a child, aged 8-14 yrs, experiencing severe hypoglycemia increasing by up to 20% with decreasing values of up to 60% of DySF. Patients of any age who had one or multiple severe hypoglycemic episodes had on average a lower DySF when compared to those with no HE. Additionally, when considering mean glucose levels, DySF/mean was a preliminary predictor of severe HE in patients with HbA1c ≤ 6.5% (p = 0.062). DySF is a dynamic, quantitative, measure of daily glucose “volatility” that separates patients, within the same strata of HbA1c, into visually distinct patient profiles. DySF can be used as a preliminary predictor of clinically severe hypoglycemia in children and “well-controlled” patients with HbA1c ≤ 6.5%.

Glycemic Profiles of Healthy Individuals with Low Fasting Plasma Glucose and HbA1c.

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

Translating Glucose Variability Metrics into the Clinic viaContinuousGlucoseMonitoring: AGraphicalUserInterface forDiabetesEvaluation (CGM-GUIDE©)

Several metrics of glucose variability have been proposed to date, but an integrated approach that provides a complete and consistent assessment of glycemic variation is missing. As a consequence, and because of the tedious coding necessary during quantification, most investigators and clinicians have not yet adopted the use of multiple glucose variability metrics to evaluate glycemic variation. We compiled the most extensively used statistical techniques and glucose variability metrics, with adjustable hyper- and hypoglycemic limits and metric parameters, to create a user-friendly Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation (CGM-GUIDE©). In addition, we introduce and demonstrate a novel transition density profile that emphasizes the dynamics of transitions between defined glucose states. Our combined dashboard of numerical statistics and graphical plots support the task of providing an integrated approach to describing glycemic variability. We integrated existing metrics, such as SD, area under the curve, and mean amplitude of glycemic excursion, with novel metrics such as the slopes across critical transitions and the transition density profile to assess the severity and frequency of glucose transitions per day as they move between critical glycemic zones. By presenting the above-mentioned metrics and graphics in a concise aggregate format, CGM-GUIDE provides an easy to use tool to compare quantitative measures of glucose variability. This tool can be used by researchers and clinicians to develop new algorithms of insulin delivery for patients with diabetes and to better explore the link between glucose variability and chronic diabetes complications.

Does Glucose Variability Influence the Relationship Between Mean Plasma Glucose and HbA1c Levels in Type 1 and Type 2 Diabetic Patients?

OBJECTIVEThe A1C-Derived Average Glucose (ADAG) study demonstrated a linear relationship between HbA1c and mean plasma glucose (MPG). As glucose variability (GV) may contribute to glycation, we examined the association of several glucose variability indices and the MPG-HbA1c relationship.RESEARCH DESIGN AND METHODSAnalyses included 268 patients with type 1 diabetes and 159 with type 2 diabetes. MPG during 3 months was calculated from 7-point self-monitored plasma glucose and continuous glucose monitoring. We calculated three different measures of GV and used a multiple-step regression model to determine the contribution of the respective GV measures to the MPG-HbA1c relationship.RESULTSGV, as reflected by SD and continuous overlapping net glycemic action, had a significant effect on the MPG-HbA1c relationship in type 1 diabetic patients so that high GV led to a higher HbA1c level for the same MPG. In type 1 diabetes, the impact of confounding and effect modification of a low versus high SD at an MPG level of 160 mg/dL on the HbA1c level is 7.02 vs. 7.43 and 6.96 vs. 7.41. All GV measures showed the same tendency.CONCLUSIONSIn only type 1 diabetic patients, GV shows a significant interaction with MPG in the association with HbA1c. This effect is more pronounced at higher HbA1c levels. However, the impact of GV on the HbA1c level in type 1 diabetes is modest, particularly when HbA1c is close to the treatment target of 7%.

A new measure of glucose variability in acute brain injured patients: Preliminary results

A new measure of glucose variability in acute brain injured patients: Preliminary results

Comparison of glucose variability measures

Glycemic excursion or glucose variability (GV) is associated with short-term ICU mortality. There is a heterogeneity among studies in using measures of GV. The objective of this study was to compare different formulas used to assess GV in predicting mortality.

Comparison of glucose variability measures

Glycemic excursion or glucose variability (GV) is associated with short-term ICU mortality. There is a heterogeneity among studies in using measures of GV. The objective of this study was to compare different formulas used to assess GV in predicting mortality.

Glucose variability measures and their effect on mortality: a systematic review

ObjectiveTo systematically review the medical literature on the association between glucose variability measures and mortality in critically ill patients.MethodsStudies assessing the association between a measure of glucose variability and mortality that reported original data from a clinical trial or observational study on critically ill adult patients were searched in Ovid MEDLINE® and Ovid EMBASE®. Data on patient populations, study designs, glucose regulations, statistical approaches, outcome measures, and glucose variability indicators (their definition and applicability) were extracted.ResultTwelve studies met the inclusion criteria; 13 different indicators were used to measure glucose variability. Standard deviation and the presence of both hypo- and hyperglycemia were the most common indicators. All studies reported a statistically significant association between mortality and at least one glucose variability indicator. In four studies both blood glucose levels and severity of illness were considered as confounders, but only one of them checked model assumptions to assert inference validity.ConclusionsGlucose variability has been quantified in many different ways, and in each study at least one of them appeared to be associated with mortality. Because of methodological limitations and the possibility of reporting bias, it is still unsettled whether and in which quantification this association is independent of other confounders. Future research will benefit from using an indicator reference subset for glucose variability, metrics that are linked more directly to negative physiological effects, more methodological rigor, and/or better reporting.

Hypoglycemia Risk and Glucose Variability Indices Derived from Routine Self-Monitoring of Blood Glucose Are Related to Laboratory Measures of Insulin Sensitivity and Epinephrine Counterregulation

the widely held assumptions that in type 1 diabetes glucose variability may correlate with insulin sensitivity and impaired epinephrine counterregulation have not been studied directly. Here we investigate possible relationships between outpatient measures of glucose variability and risk for hypoglycemia with physiological characteristics: insulin sensitivity and hypoglycemia counterregulation. thirty-four subjects with type 1 diabetes (14 women, 20 men; 37 ± 2.1 years old; glycosylated hemoglobin [HbA1c], 7.6 ± 0.21%) performed self-monitoring of blood glucose (SMBG) for a month, followed by an inpatient hyperinsulinemic euglycemic and hypoglycemic clamp. SMBG field data were used to calculate measures of glucose variability and risk of hypoglycemia, while the clamp procedure was used to evaluate insulin sensitivity and epinephrine response during induced hypoglycemia. Spearman partial correlations adjusted for age, duration of diabetes, body mass index, gender, and HbA1c were used to assess the relationship between the field indices of glucose variability and the physiological characteristics of diabetes. two glucose variability measures correlated positively (P < 0.01) with insulin sensitivity: the Average Daily Risk Range (ADRR) (ρ = 0.5) and the Glycemic Lability Index (ρ = 0.48). The Low Blood Glucose Index, a measure of the risk for hypoglycemia, and the ADRR correlated negatively with maximum epinephrine response during hypoglycemia: ρ = -0.46, P < 0.01 and ρ = -0.4, P = 0.03, respectively. higher insulin sensitivity and lower epinephrine response during hypoglycemia are related to increased glucose variability (as quantified by the ADRR), irrespective of HbA1c and other patient characteristics. Lower epinephrine relates to risk for hypoglycemia as well.

HbA1c and mean blood glucose show stronger associations with cardiovascular disease risk factors than do postprandial glycaemia or glucose variability in persons with diabetes: the A1C-Derived Average Glucose (ADAG) study

AimsIncreased glucose excursions and postprandial hyperglycaemia have been suggested as unique risk factors for cardiovascular disease (CVD) and mortality in patients with diabetes mellitus. Much of the evidence is based on a single 2 h glucose value after oral glucose tolerance testing in epidemiological studies. We examined the association between various indices of glycaemia measured during everyday activities and metabolic CVD risk factors in the A1C-Derived Average Glucose (ADAG) study.MethodsParticipants (268 with type 1 diabetes, 159 with type 2 diabetes) completed 16 weeks of intensive continuous glucose monitoring (CGM) and self-monitoring of blood glucose (SMBG). From these data, common indices of postprandial glycaemia, overall hyperglycaemia, glucose variability and HbA1c were derived. The associations between glycaemic indices and known CVD risk factors (lipids, high-sensitivity C-reactive protein and blood pressure) were explored in linear regression models.ResultsFor both diabetes types, the overall strongest associations with CVD risk factors were seen for the measures of average glycaemia (mean blood glucose and HbA1c). Associations between self-monitored postprandial and fasting glucose and CVD risk factors were weaker, but significant. Measurements of blood glucose variability showed non-significant associations. Overall, calculations based on CGM were not more informative than those based on frequent SMBG.Conclusions/interpretationMean glycaemia and HbA1c show consistent and stronger associations with CVD risk factors than fasting glucose or postprandial glucose levels or measures of glucose variability in patients with diabetes.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-010-1918-2) contains supplementary material, which is available to authorised users.

Inter-Day Glycemic Variability Assessed by Continuous Glucose Monitoring in Insulin-Treated Type 2 Diabetes Patients on Hemodialysis

Type 2 diabetes patients on chronic hemodialysis have a high prevalence of cardiovascular complications and often show a poor glycemic control. Single-spot glycemic measurements are not always meaningful, and the hemoglobin A1c (HbA1c) value does not reflect short-term variations in glucose metabolism in this patient category. Therefore, to better understand their metabolic balance, we studied a group of diabetes patients on hemodialysis by a continuous glucose monitoring (CGM) system. Twelve insulin-treated type 2 diabetes patients on hemodialysis were studied by a microdialysis-based subcutaneous glucose sensor over a period of 2 days, including the dialysis day (HD) and the following inter-dialytic period ("free" day [FD]). The mean 24-h glycemic value, the mean amplitude of glucose excursions, and the SD of mean glucose were significantly higher in the HD than the FD (186 ± 50 vs. 154 ± 25 mg/dL, P<0.05; 75 ± 22 vs. 56 ± 15 mg/dL, P<0.05; and 57 ± 6 vs. 35 ± 11 mg/dL, P<0.05, respectively). Considering the 48-h recording, there was a direct correlation between the mean glucose concentration and the HbA1c (r=0.47, P<0.05), whereas no association was observed between the measures of glucose variability and HbA1c. Insulin-treated diabetes patients on hemodialysis showed different glucose profiles between the HD and the FD. In particular, in the HD they have had an increased glycemic variability, which may represent an adjunctive risk factor for cardiovascular complications. Therefore the use of a CGM system, as a means of assessing the measures of glycemic variability, could improve the management of insulin therapy in these patients.