Glucose Variability Indices Research Articles

Relative contributions of preprandial and postprandial glucose exposures, glycemic variability, and non-glycemic factors to HbA1c in individuals with and without diabetes

Background/objectiveThere is substantial interest in dietary approaches to reducing postprandial glucose (PPG) responses, but the quantitative contribution of PPG to longer-term glycemic control (reflected in glycated hemoglobin, HbA1c) in the general population is not known. This study quantified the associations of preprandial glucose exposure, PPG exposure, and glycemic variability with HbA1c and estimated the explained variance in HbA1c in individuals with and without type 2 diabetes (T2D).Subjects/methodsParticipants in the A1c-Derived Average Glucose (ADAG) study without T2D (n = 77) or with non-insulin-treated T2D and HbA1c<6.5% (T2DHbA1c < 6.5%, n = 63) or HbA1c ≥ 6.5% (T2DHbA1c ≥ 6.5%, n = 34) were included in this analysis. Indices of preprandial glucose, PPG, and glycemic variability were calculated from continuous glucose monitoring during four periods over 12 weeks prior to HbA1c measurement. In linear regression models, we estimated the associations of the glycemic exposures with HbA1c and calculated the proportion of variance in HbA1c explained by glycemic and non-glycemic factors (age, sex, body mass index, and ethnicity).ResultsThe factors in the analysis explained 35% of the variance in HbA1c in non-diabetic individuals, 49% in T2DHbA1c < 6.5%, and 78% in T2DHbA1c ≥ 6.5%. In non-diabetic individuals PPG exposure was associated with HbA1c in confounder-adjusted analyses (P < 0.05). In the T2DHbA1c < 6.5% group, all glycemic measures were associated with HbA1c (P < 0.05); preprandial glucose and PPG accounted for 14 and 18%, respectively, of the explained variation. In T2DHbA1c ≥ 6.5%, these glycemic exposures accounted for more than 50% of the variation in HbA1c and with equal relative contributions.ConclusionsAmong the glycemic exposures, PPG exposure was most strongly predictive of HbA1c in non-diabetic individuals, suggesting that interventions targeting lowering of the PPG response may be beneficial for long-term glycemic maintenance. In T2D, preprandial glucose and PPG exposure contributed equally to HbA1c.

TSH oscillations in young patients with type 1 diabetes may be due to glycemic variability.

A relationship between thyroid dysfunction and diabetes mellitus has been described by several authors but the role of glycemic variability is still unclear. We planned the present study to evaluate the influence of glycemic variability on thyroid hormones and TSH concentrations in patients with type 1 diabetes mellitus (T1DM). Seventy-seven young patients with T1DM were enrolled and evaluated for basal glucose concentrations, HbA1c, thyroid hormones and TSH concentrations. Glucose variability was investigated by considering the standard deviation of blood glucose readings and by calculating the mean amplitude of glycemic excursions and continuous overlapping net glycemic action (CONGA). The low (LBGI) and high (HBGI) blood glucose indices were also calculated. The correlations between TSH, thyroid hormones, glycemia and HbA1c were studied in patients and in controls, whereas those between TSH, thyroid hormones and indices of glucose variability only in patients. No correlations were observed in T1DM patients between free thyroid hormones and glycemic values, HbA1c and indices of glucose variability, while an inverse correlation was observed between TSH levels and glycemic values (r=-0.27; p=0.01), CONGA index (r=-0.35; p=0.001) and HBGI (r=-0.28; p=0.01) but not with HbA1c (r=-0.1; p=0.47). Our results suggest a direct action of glycemic excursions on TSH secretion, regardless of variations of thyroid hormone concentrations. Thus, the evaluation of thyroid function through the assay of TSH concentrations in these patients should be made, if possible, by multiple samples on patients in euglycemic state to avoid underestimation or overestimation of thyroid dysfunction due to a wrong diagnosis of euthyroidism or dysthyroidism with consequent inappropriate choice of therapeutic options.

Exploring the Frequency Domain of Continuous Glucose Monitoring Signals to Improve Characterization of Glucose Variability and of Diabetic Profiles.

Continuous glucose monitoring (CGM) devices measure interstitial glucose concentrations (normally every 5 minutes), allowing observation of glucose variability (GV) patterns during the whole day. This information could be used to improve prescription of treatments and of insulin dosages for people suffering diabetes. Previous efforts have been focused on proposing indices of GV either in time or glucose domains, while the frequency domain has been explored only partially. The aim of this work is to explore the CGM signal in the frequency domain to understand if new indexes or features could be identified and contribute to a better characterization of glucose variability. The direct fast Fourier transform (FFT) and the Welch method were used to analyze CGM signals from three different profiles: people at risk of developing type 2 diabetes (P@R), T2D patients, and type 1 diabetes (T1D) patients. The results suggests that features extracted from the FFT (ie, the localization and power of the maximum peak of the power spectrum and the bandwidth at 3 dB) are able to provide a characterization for all the three populations under study compared with the Welch approach. Such preliminary results can represent a good insight for futures investigations with the possibility of building and using new indexes of glucose variability based on the frequency features.

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.

Serum but not salivary cortisol levels are influenced by daily glycemic oscillations in type 2 diabetes

Diurnal salivary and plasma cortisol variations are considered valid expression of circadian cortisol rhythmicity. The aim of this study was to assess the reliability of salivary and plasma cortisol evaluating if glycemia and glycemic oscillations may interfere with their concentration.Methods: Forty-seven type 2 diabetic patients and 31 controls were studied for glycemic profile and diurnal salivary and plasma cortisol variations on two contemporary samples taken at 08:00 a.m. and 11:00 p.m (Late Night, LN). Glucose variability was evaluated in diabetic patients by considering the standard deviation of blood glucose (BGSD) readings, by calculating the mean amplitude of glycemic excursions (MAGEs) and continuous overlapping net glycemic action (CONGA).Results: A significant correlation between LN serum cortisol and morning fasting glycemia (r = 0.78; p = 0.004) was observed in T2DM group but not in the control group (r = 0.09; p = 0.74). While LN serum cortisol significantly correlated with CONGA in diabetic patients (r = 0.50; p &lt; 0.001), LN salivary cortisol did not correlate with any indices of glucose variability. Moreover, a highly significant correlation between LN salivary and LN serum cortisol concentrations was found in control group (r = 0.80; p &lt; 0.001) but not in diabetic patients (r = 0.07; p = 0.62) .Conclusions: This study shows for the first time that late night salivary cortisol may give more information than late night plasma cortisol on the dynamic of adrenal function of type 2 diabetic patients, as it is not significantly influenced by glycemic variations.

Clinical and metabolic factors associated with chronic low-grade inflammation in type 2 diabetic patients

Aim. To identify the clinical and metabolic factors associated with serum concentration of high sensitivity C-reactive protein (hsCRP) and α1-acid glycoprotein (α1-AGP) in patients with type 2 diabetes.Material and methods. The study involved 210 patients with type 2 diabetes. Levels of hsCRP and α1-AGP were measured using ELISA and compared with those of the control (30 healthy normal individuals). Levels of acute-phase proteins, fat mass and glucose variability (GV) were compared among demographic, anthropometric, biochemical and haematological parameters. The fat mass was determined with Dual-energy X-ray absorptiometry (DEXA). GV parameters including mean amplitude of glycaemic excursions, continuous overlapping net glycaemic action (CONGA), J-index, M-value and mean absolute glucose change (MAG) were derived from continuous glucose monitoring.Results. Levels of hsCRP and α1-AGP significantly increased (p 0.0001) in patients with diabetes compared with controls. hsCRP level positively correlated with total, truncal and android fat (r = 0.34, r = 0.28 and r = 0.31; respectively, p 0.00004). α1-AGP level showed no relationship with fat mass but positively correlated with mean glucose, CONGA, M-value and MAG (r = 0.38, r = 0.36, r = 0.43 and r = 0.4; respectively, p 0.0001). Patients with the highest hsCRP levels (75 percentile) had a greater body mass index (p = 0.00009) as well as truncal and android fat mass (p = 0.04 and p = 0.03, respectively) than those with the lowest levels (25 percentile). High level of α1-AGP (75 percentile) was associated with urinary albumin/creatinine ratio (p = 0.01) and GV indices (M-value: p = 0.02, MAG: p = 0.04).Conclusions. Levels of acute-phase proteins (hsCRP and α1-AGP) increased in patients with type 2 diabetes. Levels of hsCRP were associated with fat mass; meanwhile, α1-AGP levels were associated with short-time GV in these patients. The results lend support to the notion that both obesity and enhanced GV are involved in the development of chronic low-grade inflammation associated with type 2 diabetes.

Glucose variability indices predict the episodes of nocturnal hypoglycemia in elderly type 2 diabetic patients treated with insulin

AimO determine the applicability of glucose variability (GV) indices derived from continuous glucose monitoring (CGM) data for prediction of nocturnal hypoglycemia (NH) in elderly patients with type 2 diabetes treated with insulin. MethodsWe observed 83 insulin-treated in-patients, 65–80 years of age. Blinded CGM data for 176 nights were analyzed. Daytime (06:00–22:59) mean glucose, Standard Deviation (SD), 2-h Continuous Overlapping Net Glycemic Action (CONGA2) and Mean Absolute Glucose (MAG), pre-midnight (23:00–23:59) mean glucose, SD and MAG, 24-h Mean Amplitude of Glucose Excursions (MAGE), were estimated. Pre-midnight glucose trends were estimated as the absolute difference between glucose values at 23:00 and 23:59 (deltaG). Episode of interstitial glucose ≤70mg/dL observed from 0:00 to 5:59 was considered as NH. ResultsNH was present in 68 out of 176 24-h recordings (39%). Lower daytime mean glucose and CONGA2, and higher MAG values were found in patients with NH as compared to those without (p=0.0002, p=0.0001 and p=0.02, respectively). Pre-midnight mean glucose was lower, while pre-midnight deltaG was higher in patients with NH (p<0.0001 and p=0.02). Antecedent daytime hypoglycemia increased the risk of NH (p<0.0001). In logistic regression analysis, the combination of daytime MAG and pre-midnight mean glucose was the most reliable predictor of subsequent NH (accuracy 75.6%, p=0.0004). ConclusionThe analysis of CGM-derived GV parameters could improve prediction of NH in elderly patients with type 2 diabetes treated with insulin.

First Clinical Experience with Retrospective Flash Glucose Monitoring (FGM) Analysis in South Africa: Characterizing Glycemic Control with Ambulatory Glucose Profile.

In 2014, an innovative blinded continuous glucose monitoring system was introduced with automated ambulatory glucose profile (AGP) reporting. The clinical use and interpretation of this new technology has not previously been described. Therefore we wanted to understand its use in characterizing key factors related to glycemic control: glucose exposure, variability, and stability, and risk of hypoglycemia in clinical practice. Clinicians representing affiliated diabetes centers throughout South Africa were trained and subsequently were given flash glucose monitoring readers and 2-week glucose sensors to use at their discretion. After patient use, sensor data were collected and uploaded for AGP reporting. Complete data (sensor AGP with corresponding clinical information) were obtained for 50 patients with type 1 (70%) and type 2 diabetes (30%), irrespective of therapy. Aggregated analysis of AGP data comparing patients with type 1 versus type 2 diabetes, revealed that despite similar HbA1c values between both groups (8.4 ± 2 vs 8.6 ± 1.7%, respectively), those with type 2 diabetes had lower mean glucose levels (9.2 ± 3 vs 10.3 mmol/l [166 ± 54 vs 185 mg/dl]) and lower indices of glucose variability (3.0 ± 1.5 vs 5.0 ± 1.9 mmol/l [54 ± 27 vs 90 ± 34.2 mg/dl]). This highlights key areas for future focus. Using AGP, the characteristics of glucose exposure, variability, stability, and hypoglycemia risk and occurrence were obtained within a short time and with minimal provider and patient input. In a survey at the time of the follow-up visit, clinicians indicated that aggregated AGP data analysis provided important new clinical information and insights.

Heart rate variability is associated with interstitial glucose fluctuations in type 2 diabetic women treated with insulin.

Heart rate variability (HRV) analysis is a commonly used tool for assessment of autonomic function in diabetic subjects. Nevertheless, the effects of glucose fluctuations on HRV remain to be clarified. In this study we investigated the associations of frequency-domain HRV parameters with current and antecedent interstitial glucose fluctuations in insulin-treated type 2 diabetic women at high cardiovascular risk. Sixty-seven women with type 2 diabetes, from 48 to 78 years of age, including 46 ones with cardiovascular autonomic neuropathy (CAN), underwent simultaneous continuous glucose monitoring (CGM) and Holter recording. Eight glucose variability (GV) indices, including standard deviation, 2-h continuous overlapping net glycemic action (CONGA2), lability index, J-index, mean amplitude of glucose excursions, mean absolute glucose (MAG), low blood glucose index (LBGI) and high blood glucose index (HBGI), were calculated from CGM data. The low frequency (LF) and high frequency (HF) power values were estimated on 5-min intervals at fasting and postprandial daytime periods, at night and during CGM-defined hypoglycemia. The values of LF and HF power declined after meals in diabetic women with normal autonomic function tests. Patients with CAN demonstrated blunted postprandial LF and HF reduction and diminished LF/HF ratio during daytime hypoglycemic events. Daytime LF and HF at fasting state correlated negatively with MAG derived from antecedent nocturnal CGM recordings. Positive correlation was found between fasting LF and nocturnal LBGI. The LF power during daytime hypoglycemia demonstrated negative correlations with nocturnal CONGA2, J-index, HBGI and MAG. The nocturnal HBGI and CONGA2, along with HbA1c and daily insulin dose, were predictors of LF during daytime hypoglycemia in multiple regression analysis. Both postprandial and antecedent nocturnal glucose fluctuations affect daytime frequency-domain HRV parameters in insulin-treated type 2 diabetic women. In patients with increased GV the results of short-term assessment of HRV should be interpreted with caution. Fasting state rather than postprandial one seems to be preferable for HRV estimation.

Time in blood glucose range 70 to 140 mg/dl >80% is strongly associated with increased survival in non-diabetic critically ill adults.

IntroductionHyperglycemia, hypoglycemia and increased glucose variability are independently associated with increased risk of death in critically ill adults. The relationship between time in targeted blood glucose range (TIR) and mortality is not well described and may be a factor that has confounded the results of the major interventional trials of intensive insulin therapy.MethodsWe conducted a retrospective analysis of prospectively collected data involving 3,297 patients with intensive care unit (ICU) lengths of stay (LOS) of ≥1.0 day who were admitted between 1 January 2009 and 31 December 2013 to a single mixed medical-surgical ICU. We investigated the relationship between TIR 70 to 140 mg/dl with mortality and compared outcomes of non-diabetics (NON) and individuals with diabetes mellitus (DM), including stratifying by TIR above (TIR-hi) and below (TIR-lo) the median value for the NON and DM groups.ResultsThere were 85,799 blood glucose (BG) values for the NON group and 32,651 for the DM group, and we found that 75.5% and 54.8%, respectively, were between 70 and 140 (P <0.0001). The median (interquartile range) TIR (%) values for the NON and DM groups were 80.6% (61.4% to 94.0%) and 55.0% (35.5% to 71.1%), respectively (P <0.0001). For the NON group, mortality was 8.47% and 15.71% for TIR-hi and TIR-lo, respectively (P <0.0001). For the DM group, mortality was 16.09% and 14.44% for TIR-hi and TIR-lo, respectively (P = NS). We observed similar relationships for the NON group when we stratified by ICU LOS or severity of illness, especially in the most severely ill patients. There was a cumulative interaction of indices of hypoglycemia, hyperglycemia or glucose variability with TIR. Multivariable analysis demonstrated, for the NON group, that TIR-hi was independently associated with increased survival (P =0.0019). For the NON group, the observed-to-expected mortality ratios for TIR-hi and TIR-lo, based on Acute Physiology and Chronic Health Evaluation IV methodology, were 0.53 and 0.78, respectively. In contrast, among those in the DM group, there was no clear relationship between TIR 70 to 140 mg/dl and survival.ConclusionsIndependently of ICU LOS and severity of illness, TIR 70 to 140 mg/dl >80% is strongly associated with survival in critically ill patients without diabetes. These findings have implications for the design of clinical protocols for glycemic control in critically ill patients as well for the design of future interventional trials of intensive insulin therapy.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-015-0908-7) contains supplementary material, which is available to authorized users.

Serum but not salivary cortisol levels are influenced by daily glycemic oscillations in type 2 diabetes.

Diurnal salivary and plasma cortisol variations are considered valid expression of circadian cortisol rhythmicity. The aim of this study was to assess the reliability of salivary and plasma cortisol and if glycemia and glycemic oscillations may interfere with their concentration. Forty-seven type 2 diabetic patients and 31 controls were studied for glycemic profile and diurnal salivary and plasma cortisol variations on two contemporary samples taken at 08:00 a.m.-11:00 p.m (Late Night, LN). Glucose variability was evaluated in diabetic patients by considering the standard deviation of blood glucose (BGSD) readings, by calculating the mean amplitude of glycemic excursions (MAGEs) and continuous overlapping net glycemic action (CONGA). A significant correlation between LN serum cortisol and morning fasting glycemia (r=0.78; p=0.004) was observed in T2DM group but not in the control group (r=0.09; p=0.74). While LN serum cortisol significantly correlated with CONGA in diabetic patients (r=0.50; p<0.001), LN salivary cortisol did not correlate with any indices of glucose variability. Moreover, a highly significant correlation between LN salivary and LN serum cortisol concentrations was found in control group (r=0.80; p<0.001) but not in diabetic patients (r=0.07; p=0.62). This study shows for the first time that LN salivary rather than plasma cortisol may give information on the dynamics of adrenal function of type 2 diabetic patients, as it is not significantly influenced by glycemic variations. However, our preliminary results need to be confirmed by further studies with more complete evaluations including many more patients.

Clinical observation on the combined therapy of sitagliptin with insulin for patients with brittle diabetes

To observe the clinical efficacy of sitagliptin plus insulin on patients with brittle diabetes and to determine the effect of the combined therapy on glucagon secretion. This randomized, double-blinded and placebo-controlled trial included 30 patients with brittle diabetes. Participants were randomly assigned (1:1) to receive the treatment of either sitagliptin plus insulin or placebo plus insulin for 12 weeks. The blood glucose, hemoglobin A1c, insulin dose, C-peptide, glucagon, glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP) and other parameters were determined. After 12 weeks of treatment, blood glucose was controlled better by sitagliptin plus insulin (P<0.01). The patients had significantly lower glucose variability indices, lower daily insulin requirement and hemoglobin A1c in the group of sitagliptin plus insulin (P<0.01). After steamed bun test, past-meal GLP-1 levels at 30 min were higher (P<0.01) while GIP levels were lower (P<0.01), with glucagon suppression in the sitagliptin plus insulin group. No significant change was observed at any time point in placebo plus insulin group. Sitagliptin significantly decreases blood glucose level and blood glucose fluctuation, which may contribute to the ability of sitagliptin in decreasing glucagon secretion.

Parsimonious Description of Glucose Variability in Type 2 Diabetes by Sparse Principal Component Analysis.

Abnormal glucose variability (GV) is a risk factor for diabetes complications, and tens of indices for its quantification from continuous glucose monitoring (CGM) time series have been proposed. However, the information carried by these indices is redundant, and a parsimonious description of GV can be obtained through sparse principal component analysis (SPCA). We have recently shown that a set of 10 metrics selected by SPCA is able to describe more than 60% of the variance of 25 GV indicators in type 1 diabetes (T1D). Here, we want to extend the application of SPCA to type 2 diabetes (T2D). A data set of CGM time series collected in 13 T2D subjects was considered. The 25 GV indices considered for T1D were evaluated. SPCA was used to select a subset of indices able to describe the majority of the original variance. A subset of 10 indicators was selected and allowed to describe 83% of the variance of the original pool of 25 indices. Four metrics sufficient to describe 67% of the original variance turned out to be shared by the parsimonious sets of indices in T1D and T2D. Starting from a pool of 25 indices assessed from CGM time series in T2D subjects, reduced subsets of metrics virtually providing the same information content can be determined by SPCA. The fact that these indices also appear in the parsimonious description of GV in T1D may indicate that they could be particularly informative of GV in diabetes, regardless of the specific type of disease.

Association between Visit-to-Visit Glucose Variability and Cognitive Function in Aged Type 2 Diabetic Patients: A Cross-Sectional Study.

Background and PurposeDiabetes is associated with cognitive decline as well as the development of dementia. Although mean blood glucose levels are typically used to assess the status of diabetic patients, glucose variability is also involved in the manifestation of macro- and microvascular complications in this population. Thus, the present study sought to determine whether visit-to-visit glucose variability contributes to cognitive decline in patients with type 2 diabetes.MethodsThe present study assessed 68 patients with type 2 diabetes using several validated neuropsychological measures. All patients had no cerebrovascular disease, history of hypoglycemia, psychiatric conditions, or other medical illnesses. Standard deviations (SDs) and coefficients of variance (CVs) of the patients’ blood glucose (after fasting and 2 hours postprandial; FBS and PP2), and glycated hemoglobin (HbA1c) values were used as indices of glucose variability. The cognitive outcome parameters were transformed with z-scores and entered into a multiple linear regression model that included educational status, age, sex, vascular risk factors, and mean glucose parameters as covariates.ResultsThe mean age of the total patient population was 70.9 years; 46 (67.6%) of the patients were men, and the median follow-up duration at our endocrinology outpatient clinic was 4.8 years. The mean FBS and PP2 glucose levels of the patients were 132 mg/dL and 199 mg/dL, respectively, and the mean HbA1c level was 8.0%. A univariable analysis revealed that only the PP2 value was associated with the Mini-Mental State Examination (MMSE) score, and multivariable analysis revealed that a high SD and/or CV for PP2 glucose were associated with low scores on the Rey Complex Figure Copy test and/or the Verbal Learning Test. Additionally, a high SD and a higher CV for HbA1c level were significantly associated with low MMSE and Digit Span test scores even after adjusting for mean HbA1c values.ConclusionsThe present data indicate that a greater degree of visit-to-visit glucose variability influenced specific types of cognitive function in type 2 diabetic patients independently of mean blood glucose levels. Future studies should focus on whether reductions in glycemic variability will improve the cognitive decline observed in type 2 diabetic patients.

Increased glycemic variability and decrease of the postprandial glucose contribution to HbA1c in obese subjects across the glycemic continuum from normal glycemia to first time diagnosed diabetes

ObjectiveThe contribution of postprandial glycemia (PPG) to hyperglycemia has been shown to decrease as HbA1c increased in type 2 diabetic patients. This study aimed at examining, in a series of overweight/obese patients without known glycemic disorder, the contribution of PPG to a “relative” hyperglycemia (glucose values ≥5.5mmol/L) and the presence of glycemic variability according to HbA1c levels. MethodsSeventy overweight/obese inpatients (body mass index 35.2±6.8kg/m2) without known glycemic disorder were included. Participants were classified according to an oral glucose tolerance test (according to the American Diabetes Association criteria) as patients with normoglycemia (n=33), with intermediate hyperglycemia (n=24) or diabetes (n=13). They were separated into HbA1c quartiles (Q1 to Q4). A 24hour continuous glucose monitoring was used under a 1800kcal diet and minimal physical activity. We assessed PPG contribution (3hour period after each meal) to the “relative” 24hour hyperglycemia (glucose values ≥5.5mmol/L); the remaining time was considered as the fasting/post-absorptive period. ResultsHbA1c range was from 5.1% to 7.4% (32 to 57mmol/mmol). From the lowest to the highest HbA1c quartile, the area under the curve (AUC) for the “relative” hyperglycemia presented a 17-fold increase for the fasting/post-absorptive (p<0.001) period and a 7-fold increase postprandially (p<0.001). The percent of PPG contribution to the “relative” hyperglycemia was calculated with the following formula [100×(postprandial 3hour AUC−3h AUC for a constant 5.5mmol/L glycemia)/(total 24h AUC−24h AUC for constant 5.5mmol/L glycemia)] and decreased from Q1 to Q4 of HbA1c (81.2%, 66%, 65.8%, 57%; p<0.001). Increasing HbA1c quartiles were associated with higher daily mean blood glucose level (p<0.001) and higher levels of daily glucose variability indices, including mean amplitude of glycemic excursions (p<0.01). ConclusionsIn overweight/obese patients, HbA1c was associated with lower PPG contribution to “relative” hyperglycemia and greater glycemic variability. The present findings support the importance of postprandial period in glycemic exposure even before the appearance of diabetes.

Glucose variability indices in type 1 diabetes: parsimonious set of indices revealed by sparse principal component analysis.

Continuous glucose monitoring (CGM) time-series are often analyzed, retrospectively, to investigate glucose variability (GV), a risk factor for the development of complications in type 1 diabetes (T1D). In the literature, several tens of different indices for GV quantification have been proposed, but many of them carry very similar information. The aim of this article is to select a relatively small subset of GV indices from a wider pool of metrics, to obtain a parsimonious but still comprehensive description of GV in T1D datasets. A pool of 25 GV indices was evaluated on two CGM time-series datasets of 17 and 16 T1D subjects, respectively, collected during the European Union Seventh Framework Programme project "Diadvisor" (2008-2012) in two different clinical research centers using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus. After the indices were centered and scaled, the Sparse Principal Component Analysis (SPCA) technique was used to determine a reduced set of metrics that allows preserving a high percentage of the variance of the whole original set. In order to assess whether or not the selected subset of GV indices is dataset-dependent, the analysis was applied to both datasets, as well as to the one obtained by merging them. SPCA revealed that a subset of up to 10 different GV indices can be sufficient to preserve more than the 60% of the variance originally explained by all the 25 variables. It is remarkable that four of these GV indices (i.e., Index of Glycemic Control, percentage of Glycemic Risk Assessment Diabetes Equation score due to euglycemia, percentage Coefficient of Variation, and Low Blood Glucose Index) were selected for all the considered T1D datasets. The SPCA methodology appears a suitable candidate to identify, among the large number of literature GV indices, subsets that allow obtaining a parsimonious, but still comprehensive, description of GV.

Influence of Dialysis on the Glucose Profile in Patients with Diabetes: Usefulness of Continuous Glucose Monitoring

We sought to investigate the impact of dialysis on glucose profiles of diabetic patients using continuous glucose monitoring (CGM). The study included 33 hemodialyzed patients with diabetes (14 females and 19 males; mean age: 66±8 years; patients with type 2 diabetes: 30; mean duration of dialysis: 3.8±2.6 years) who were under insulin treatment. After a run-in period, CGM was performed for 48 h, including a dialysis session. Three CGM sessions were proposed for each patient over a 3-month period. CGM results were analyzed during and after dialysis at 6 different time points. Moreover, data were analyzed in 7 different day periods according to meals. Of the 99 CGM available, 21 were excluded because of technical issues or patient refusal. The CGM results indicated that mean glucose values (7.5±2.5 mmol/l vs. 9.4±1.9 mmol/l; p<0.001) and variability indices (p<0.001) were lower, whereas the frequency of hypoglycemia (4.4±9.6% vs. 2.1±7.9%; p<0.001) was higher during hemodialysis sessions. Significant differences were observed in glucose values only before and 2 h after breakfast (p<0.001). Compared with other day periods, glucose values were lower during the second half of the night and higher before and after dinner (p<0.001). In summary, CGM allows the identification of a particular glucose profile in hemodialyzed diabetic patients. CGM seems feasible and clinically useful for the analysis of glucose profiles in this group of patients.

Poincaré Plot Quantification for Assessing Glucose Variability from Continuous Glucose Monitoring Systems and a New Risk Marker for Hypoglycemia: Application to Type 1 Diabetes Patients Switching to Continuous Subcutaneous Insulin Infusion

The Poincaré plot (PCP) is a valuable tool for describing glucose variability (GV) from continuous glucose monitoring (CGM) but remains only visual and qualitative. The aim of this work was to validate metrics for the geometry of the PCP in type 1 diabetes and to apply them to the study of a series of patients switching to continuous subcutaneous insulin infusion (CSII). We reviewed the CGM profiles of 44 patients with type 1 diabetes. A subgroup (n=13) used CGM before and after 6 months on CSII. Additionally, we prospectively collected seven recordings from healthy controls. The new PCP metrics were correlated with hypoglycemia and classical GV indices and were compared between groups. SDs related to the PCP fitting ellipse (SD1, SD2) and area and shape of the fitting ellipse (SFE) were all higher in diabetes patients than in the controls and decreased significantly on CSII. SD1 represented short-term GV and was equivalent to continuous overlapping net glycemic action (CONGA). SD2 represented long-term GV and correlated with the SD of glucose levels (r ≥ 0.98), mean of daily differences (r ≥ 0.91), and mean amplitude of glycemic excursions (r ≥ 0.88). SFE correlated positively with CONGA at 1 h but not with the other indices and was inversely correlated with hypoglycemic episodes (Spearman's ρ=-0.42), independently of the coefficient of variation and the Low Blood Glucose Index in a multivariate analysis (partial r=-0.34). PCP metrics are correlated with known GV indices and may be used for the study of CGM recording series in type 1 diabetes. SFE is a new risk marker for hypoglycemia.

Glucose Variability Assessed by Low Blood Glucose Index Is Predictive of Hypoglycemic Events in Patients With Type 1 Diabetes Switched to Pump Therapy

OBJECTIVETo determine whether subgroups of type 1 diabetic patients with different glucose variability indices respond differently to continuous subcutaneous insulin infusion (CSII) in terms of reduced hypoglycemic events.RESEARCH DESIGN AND METHODSWe studied 50 adults with long-standing type 1 diabetes switched to CSII because of persistently high A1C or frequent hypoglycemia despite well-managed intensive basal-bolus therapy. We compared A1C, hypoglycemic events, and glucose variability from self-monitoring of blood glucose profiles at baseline and after 6 months of CSII. Regression analysis was performed to identify predictors of response.RESULTSIn multivariate analysis, baseline low blood glucose index (LBGI) was the best independent predictor of hypoglycemia outcome on CSII (R2 = 0.195, P = 0.0013). An ROC curve analysis demonstrated a sensitivity of 70.8% (95% CI 48.9–87.4) and specificity of 73.1% (52.2–88.4) by using the LBGI cutoff of 3.34 as predictor of reduction of hypoglycemia on CSII. By grouping patients by LBGI tertiles, we found a 23.3% reduction in hypoglycemic events (<60 mg/dL [3.3 mmol/L]) in the third tertile (range 4.18–9.34) without change in A1C (P < 0.05). Conversely, the first tertile (range 0.62–2.05) demonstrated the greatest A1C reduction, −0.99% (P = 0.00001), but with increasing hypoglycemia.CONCLUSIONSBaseline LBGI predicts the outcome of type 1 diabetic patients who switch to CSII in terms of hypoglycemia.

The importance of HbA1c and glucose variability in patients with type 1 and type 2 diabetes: outcome of continuous glucose monitoring (CGM)

Glucose variability has recently been investigated in diabetic patients in several studies, but most of them considered only a few variability indicators and did not systematically correlate them with patients' HbA1c levels and other important characteristics. In thus study, the correlations between HbA1c levels and metabolic control (average glucose, AG), glucose variability (SD, CONGA, MAGE, MODD, BG ROC), hyperglycemia (HBGI), hypoglycemia (LBGI) and postprandial (AUC PP) indices were investigated in patients with type 1 and type 2 diabetes. The study involved 68 patients divided into 3 groups as follows: 35 patients had type 1 diabetes (group 1); 17 had type 2 diabetes and were taking multiple daily injections (MDI) of insulin (group 2); and 16 patients had type 2 diabetes treated with OHA and/or basal insulin (group 3). The indicators were obtained over at least 48 h using a continuous glucose monitoring (CGM) system. HbA1c levels were measured at the baseline and after CGM. HbA1c correlated significantly with AG (r = 0.74), AUC PP (r = 0.69) and HBGI (r = 0.74), but only in type 1 diabetic patients. Patients with longstanding disease and type 1 diabetes had a greater glucose variability, irrespective of their HbA1c levels. Insulin therapy with MDI correlated strongly with HbA1c, but not with glucose variability. HbA1c levels identify states of sustained hyperglycemia and seem to be unaffected by hypoglycemic episodes or short-lived glucose spikes, consequently revealing shortcomings as a "gold standard" indicator of metabolic control. Glucose variability indicators describe the glucose profile of type 1 diabetic patients and identify any worsening glycemic control (typical of longstanding diabetes) more accurately than HbA1c tests.