Continuous Overall Net Glycemic Action Research Articles

THU341 Associations Of Longitudinal Nighttime Sleep Characteristics With Nighttime/Daytime Glucose Values As Assessed By Continuous Glucose Monitoring In Type 2 Diabetes

Abstract Disclosure: Y. Li: None. K.K. Danielson: None. J.C. Park: None. J. McAnany: None. F.Y. Chau: None. B. Prasad: None. E.C. Hanlon: None. S. Reutrakul: None. Background: Sleep disturbances have been shown to be associated with incident diabetes and poor glycemic control. Recently, evidence emerged that nightly sleep may affect glucose levels both during that night and the following day. In this study, we explored the association of serial measures of nightly sleep parameters with night/day glucose levels in patients with type 2 diabetes (T2D). Methods: Baseline data of 28 T2D patients participating in an ongoing study exploring the relationship between diabetes retinopathy (DR) and sleep (NCT 04547439) were included. Sleep was objectively recorded using 14-days actigraphy. Sleep parameters nightly included sleep duration, sleep efficiency, wake time after sleep onset (WASO), and mid-sleep time (MST, time between sleep time and waketime). Glucose was recorded using blinded continuous glucose monitors (CGM). CGM data were divided into nighttime and daytime glucose values, utilizing the sleep time from actigraphy. CGM parameters included mean glucose, percent time spent in range (TIR%; glucose values 70-180 mg/dL), percent time above range (>180 mg/dL), and percent time below range (<70 mg/dL). Glucose variability indices included percent coefficient of variation (%CV) and Continuous Overall Net Glycemic Action (CONGA). Longitudinal mixed linear regression models, adjusting for age, sex, insulin use and DR status, were used to determine the associations between sleep parameters and nighttime/subsequent daytime CGM values. Associations were considered statistically significant at p<0.01 due to the multiple comparisons. Results: Mean (SD) age was 55.0 (6.5) years, A1C was 7.8 (1.5)%, 57% of the participants were female and 60.1% had DR. Average sleep duration was 6.1 (1.0) hours, sleep efficiency was 79.8 (9.6)%, WASO was 49.0 (27.1) minutes and MST was 3:25 (1:23) AM. There were a total of 281 nights and 254 days of CGM recordings. For nighttime CGM analyses, sleep duration (mins) was significantly longer in %TIR of ≥70% vs. <70% (β=57, SE=13, p=0.0005), but there were no associations of duration with other CGM parameters. WASO (log) was positively associated with CONGA (β=2.3, SE=0.9, p=0.008). Earlier MST was associated with greater glucose variability including higher CONGA (β=−1.6, SE=0.4, p<0.0001), and %CV (β=−1.6, SE=0.4, p<0.0001). For the following day CGM analyses, only the association between earlier MST and greater time spent below range (β=−0.7, SE=0.2, p=0.006) was significant. Conclusion: Longer nightly sleep duration was associated with higher nighttime %TIR, while poor sleep continuity was associated with higher nighttime glucose variability. In contrast to previous findings, earlier sleep timing was associated with unfavorable glucose profiles both during nighttime and daytime. These pilot data highlighted the associations between nighttime sleep and metabolic control, and should be explored in a larger cohort. Presentation: Thursday, June 15, 2023

The efficacy and safety of combined GLP-1RA and basal insulin therapy among inadequately controlled T2D with premixed insulin therapy.

This study investigated the effect of a combination of glucagon-like peptide-1 receptor agonist (GLP-1 RA) and basal insulin (BI) in poorly controlled type 2 diabetes mellitus previously treated with premixed insulin. The possible therapeutic benefit of the subject is mainly hoped to provide a direction for optimizing treatment options to reduce the risk of hypoglycemia and weight gain. A single-arm, open-label study was conducted. The antidiabetic regimen was switched to GLP-1 RA plus BI to replace previous treatment with premixed insulin in type 2 diabetes mellitus subjects. After 3 months of treatment modification, GLP-1 RA plus BI was compared for superior outcomes by continuous glucose monitoring system. There were 34 subjects at the beginning, 4 withdrew due to gastrointestinal discomfort, and finally 30 subjects completed the trial, of which 43% were male; the average age was 58 ± 9 years old, and the average duration of diabetes was 12 ± 6 years, the baseline glycated hemoglobin level was 8.6 ± 0.9 %. The initial insulin dose of premixed insulin was 61 ± 18 units, and the final insulin dose of GLP-1 RA + BI was 32 ± 12 units (P < .001). Time out of range (from 59%-42%), time-in-range (from 39%-56%) as well as glucose variability index including standard deviation also improved, mean magnitude of glycemic excursions, mean daily difference and continuous population in continuous glucose monitoring system, continuous overall net glycemic action (CONGA). Also noted was a decrease in body weight (from 70.9 kg-68.6 kg) and body mass index (all P values < .05). It provided important information for physicians to decide to modify therapeutic strategy as individualized needs.

Взаимосвязь показателей вариабельности гликемии и нарушений сердечного ритма у пациентов с сахарным диабетом 1 типа

Objective: To study indicators of glycemic variability (GV) and their relationship with cardiac arrhythmias in patients with type 1 diabetes mellitus according to glucocardiomonitoring data. Design: Cross-sectional study. Materials and methods. The study included 72 patients with type 1 diabetes without previously diagnosed cardiovascular pathology. Of these, 34 patients received insulin pump therapy, 38 received multiple insulin injections. The average age of patients was 39.7 ± 15.9 years, the average duration of type 1 diabetes was 15.4 ± 10.8 years. The level of glycated hemoglobin (HbA1c) was determined in patients, and according to continuous glucose monitoring (CGM), GV indices were calculated: SD (standard deviation), CONGA (continuous overall net glycemic action), LI (lability index), MAGE (mean amplitude of glycemic excursions), MAG (mean absolute glucose). Simultaneously with CGM, Holter monitoring of the electrocardiogram was carried out: heart rate (HR), the presence and time of occurrence of arrhythmias, variability of the corrected QT interval (QTc), and the presence of ischemic changes were assessed. A correlation analysis of the relationships between GV levels and heart rhythm disorders was carried out. Results. Regardless of the type of insulin therapy, GV indicators in patients with type 1 diabetes were higher than the reference values for healthy individuals. Only 19 (26%) patients reached the target HbA1c level. In 62 (86%) people, various heart rhythm disturbances were registered, in 30 (42%) of patients there was a decrease in the circadian index (CI) of heart rate, and in 14 (19%) there was a prolongation of the QTc interval. The results of the correlation analysis showed that with an increase in GV, the HR CI decreased and the frequency of supraventricular and ventricular extrasystoles increased. Conclusion. The detected changes in heart rhythm against the background of increased GV in patients with type 1 diabetes indicate a high risk of developing a cardiovascular process. Strict glycemic control and normalization of GV can better reduce the risk of cardiac arrhythmia and the development of DCAN in patients with type 1 diabetes. Keywords: type 1 diabetes mellitus, glycemic variability, cardiovascular disease, diabetic cardiovascular autonomic neuropathy

Effects of structured Ramadan Nutrition Plan on glycemic control and variability using continuous glucose monitoring in individuals with type 2 diabetes: A pilot study

Background and aimsContinuous glucose monitoring (CGM) has been increasingly used in recent years to evaluate glycemic control and variability in individuals with diabetes observing Ramadan fasting. However, the effectiveness of the Ramadan Nutrition Plan (RNP) in individuals with type 2 diabetes (T2D) using CGM-derived measures has not been investigated. The study aimed to evaluate the effects of structured RNP versus standard care using CGM in individuals with T2D. MethodsThis parallel non-randomized interventional study with patients’ preference design involved 21 individuals with T2D (mean age: 49 ± 10 years, BMI: 30.0 ± 6.2 kg/m2). Participants chose to receive either structured RNP (sRNT; structured Ramadan Nutrition Therapy group; n = 14) or standard care (SC; n = 7). Participants wore CGM 5 days before Ramadan and during Ramadan. CGM-derived measures of glycemic variability were calculated using Glyculator version 2.0. ResultsCompared to the SC group, the sRNT group significantly reduced their fasting blood glucose levels, HbA1c, total cholesterol, diastolic blood pressure, and increased dietary fiber intake. CGM data showed the sRNT group had significantly lower average sensor glucose, peak sensor value, estimated A1c, percentage and duration of time-above-range, J-index, mean amplitude of glycemic excursion (MAGE), and continuous overall net glycemic action (CONGA); and a significantly higher percentage of time-in-range (TIR). ConclusionsThe structured RNP significantly improved clinical outcomes, glycemic control and variability in individuals with T2D. The study highlights the importance of utilizing CGM sensor data to monitor glycemic excursions during Ramadan fasting. Adequately powered randomized controlled trials are needed to confirm the findings.

Evaluating perioperative glycemic status after different types of pancreatic surgeries via continuous glucose monitoring system: a pilot study.

Perioperative glycemic status after pancreatic surgery has never been described. However, it's essential for optimal perioperative glucose management and understanding the pathogenesis of new-onset diabetes mellitus (NODM) after pancreatectomy. Continuous glucose monitoring (CGM) system provides us a helpful tool for closely monitoring and studying perioperative glucose change. This study tried to describe and compare perioperative glucose level and glycemic variability between different types of pancreatic surgeries via CGM device. This study was designed as a prospective observational study. Eighteen patients were enrolled and were grouped by different types of surgery received: control group (CTRL), pancreaticoduodenectomy (PD), distal pancreatectomy (DP), and total pancreatectomy (TP). CGM devices were implanted and initiated right after the surgery. Mean glucose value (MGV), coefficient of variation (CV), mean of daily difference (MODD), continuous overall net glycemic action (CONGA), and time above range (TAR)/time below range (TBR) was compared between groups to assess glucose level and glycemic variability. TP showed the highest MGV and CV among all groups (P<0.001), while CTRL showed the lowest (P<0.001). PD and DP had similar MGV and CV lower than TP but higher than CTRL (P<0.001). TP had the highest MODD and CONGA, CTRL had the lowest, but no significant differences were found between groups. TP had the highest TAR (24.29%) and the lowest TBR (1.28%), while the control group showed the opposite. The differences in TAR/TBR between groups were all significant (P<0.05). TP had the highest mean glucose level and the greatest glycemic variability. PD and DP had similar results: a higher mean glucose level than control but lower than TP. For glycemic variability, PD and DP seemed to have a near-normal result resembling the control group. CGM is useful for glucose monitoring in the perioperative management of pancreatic surgery.

Effect of a single bout of morning or afternoon exercise on glucose fluctuation in young healthy men.

The timing of exercise plays an important role in the effect of the exercise on physiological functions, such as substrate oxidation and circadian rhythm. Exercise exerts different effects on the glycemic response to exercise and meal intake depending on when the exercise performed. Here, we comprehensively investigated the effects of the timing (morning or afternoon) of exercise on glucose fluctuation on the basis of several indices: glycemic variability over 24 h (24‐h SD), J‐index, mean amplitude of glucose excursions (MAGE), continuous overall net glycemic action (CONGA), and detrended fluctuation analysis (DFA). Eleven young men participated in 3 trials in a repeated measures design in which they performed a single bout of exercise at 60% of their maximal oxygen uptake for 1 h beginning either at 7:00 (morning exercise), 16:00 (afternoon exercise), or no exercise (control). Glucose levels were measured using a continuous glucose monitoring system (CGMs). Glucose fluctuation was slightly less stable when exercise was performed in the afternoon than in the morning, indicated by higher CONGA at 2 h and α2 in DFA in the afternoon exercise trial than in the control trial. Additionally, decreased stability in glucose fluctuation in the afternoon exercise trial was supported by the descending values of the other glucose fluctuation indices in order from the afternoon exercise, morning exercise, and control trials. Meal tolerance following exercise was decreased after both exercise trials. Glucose levels during exercise were decreased only in the afternoon exercise trial, resulting in less stable glucose fluctuations over 24 h.

Glycemic Variability and Diabetic Neuropathy in Young Adults With Type 1 Diabetes.

Background: Glycemic variability (GV) may attribute to the pathogenesis of diabetic neuropathy. The aim of this cross-sectional study was to investigate the association between GV and distal symmetric polyneuropathy (DSPN) and cardiovascular autonomic neuropathy (CAN) in a Danish population of young adults with type 1 diabetes.Methods: Young adults between 18 and 24 years with type 1 diabetes were included in this cross-sectional study. CAN was assessed by cardiovascular autonomic reflex tests (CARTs) and heart rate variability (HRV). DSPN was assessed by light pressure, pain and vibration perception, electrochemical skin conductance, sural nerve conduction velocity (SNCV), and amplitude potential (SNAP). GV were obtained by continuous glucose monitoring including coefficient of variation (CV), SD, continuous overall net glycemic action (CONGA), and mean amplitude of glucose excursions (MAGE).Results: The study comprised 133 young adults (43.6% males), mean age of 22 years (SD 1.6). Unadjusted, higher CV was associated with a decreased risk of sural nerve conduction (P = 0.03), abnormal SNAP (P = 0.04) and incidents of definite CAN (P = 0.04). Likewise, higher CONGA was associated with increasing incidents of subclinical DSPN (P = 0.03), abnormal SNAP (P = 0.01), and SNCV (P = 0.02). However, both associations were not statistically significant in the fully adjusted model. Higher MAGE was associated with slightly increasing measures of HRV (P = 0.03) but only when fully adjusted. When correcting for multiple tests significance was lost. A significant association was found between HbA1c and measures of both DSPN (P < 0.02) and HRV (P < 0.03) in fully adjusted models.Conclusions: No significant associations between GV and diabetic neuropathy were found after adjusting for risk factors and multiple tests. This suggests that GV may not be a risk factor for diabetic neuropathy in young adults with type 1 diabetes. However, long-term effects of GV excursions may still play a role in the pathogenic mechanisms leading to neuropathy in later life.

Alterations in the Levels of Circulating and Endothelial Progenitor Cells Levels in Young Adults with Type 1 Diabetes: A 2-Year Follow-Up from the Observational METRO Study.

PurposeType 1 diabetes is associated with high risk of cardiovascular disease (CVD). Reduced levels of circulating progenitor cells (CPCs) and endothelial progenitor cells (EPCs) have been indicated as a risk factor for adverse cardiovascular outcomes and death in people at high cardiovascular risk. The aim of the present study was to evaluate the change in CPCs and EPCs levels in a population of young type 1 diabetic patients treated with intensive insulin regimen over a period of 2 years.Patients and MethodsA total of 204 type 1 diabetic patients, of whom 84 treated with insulin pump (CSII) and 120 with multiple daily insulin injections (MDI), completed a 2-year follow-up. Clinical measurements, including the indices of glycemic control and glucose variability, were collected at baseline and after 2 years. Both CPC and EPC cell count were assessed by flow cytometry.ResultsMean age of participants was 24.5 years and mean diabetes duration was 13.6 years. After 2 years, we found a significant reduction of HbA1c (−0.3% versus baseline, P <0.001), associated with decrease in mean amplitude of glucose excursion (MAGE) (−0.5 mmol/L versus baseline, P<0.001), continuous overall net glycemic action (CONGA) (−0.2 mmol/L versus baseline, P=0.006), and blood glucose standard deviation (BGSD) (−0.2 mmol/L versus baseline, P<0.001). The number of all EPCs phenotypes, but not CPC cell count, significantly raised up in the entire population, with higher increase in CSII group. MAGE resulted as an independent predictor for increased levels of both CD34+ (P = 0.020) and CD34+KDR+ (P = 0.004) cell count in the whole population.ConclusionOver a 2-year follow-up, young type 1 diabetic patients showed an increase in circulating EPCs levels, which was higher in patients with CSII. Glucose variability resulted as an independent predictor of the raised levels of EPCs in this selected population.

Characteristics of glucose metabolism indexes and continuous glucose monitoring system (CGMS) in patients with insulinoma

AimsAnalyze the clinical applicability of glucose metabolism indexes and continuous glucose monitoring data on the qualitative diagnosis of insulinoma.MethodsInvolve 22 patients with insulinoma (insulinoma group), 11 patients with hypoglycemia (hypoglycemia group) and 31 people with normal glucose tolerance (control group). HbA1c, fasting blood glucose (FBG), insulin (FINS) and C-peptide (FCP) was tested. Using CGMS to monitor the blood glucose for three consecutive days and selecting the monitoring data of 24 h thereof, figuring out, with the aid of EasyGV Version 9.0, the mean glucose (MG), the standard deviation (SD) of blood glucose, CONGA (continuous overall net glycemic action), J-Index, LI (Lability Index), LBGI (Low Blood Glucose Index), HBGI (High Blood Glucose Index), GRADE (glycaemic risk assessment diabetes equation), MAGE (mean aplitude of glycaemic excursions), M value, MAG (mean absolute glucose).Results(1) FBG and LBG of insulinoma group are lower than those of control group and those of hypoglycemia group while FINS and FCP of insulinoma group are markedly higher than those of the other two groups; (2) the MG and CONGA of insulinoma group are lower than those of control group and its indexes like ST, LI, LBGI, GRADE, MAGE, M value and MAG are higher than those of control group; there are differences between the indexes of insulinoma group and those of hypoglycemia group in CONGA (lower than that of hypoglycemia group), LBGI (higher than that of hypoglycemia group), and M value (higher than that of hypoglycemia group). By drawing the ROC curve and calculating Youden index, the cut-off values of LBGI, M value, CONGA are respectively as 4.06, 7.79, 4.38, and the best index of differential diagnosis is LBGI.ConclusionContinuous glucose monitoring data can be used to diagnose insulinoma and blood glucose fluctuation indicators such as LBGI, M value, CONGA might be useful to identify insulinoma.

THE ROLE OF GLYCEMIA VARIABILITY IN THE PROCESSES OF CELLULAR AND VASCULAR AGEING IN PATIENTS WITH TYPE 2 DIABETES

Aim. To investigate on the presence and relation characteristics of glycemia variability (GV) and vascular wall parameters, chronic inflammation, oxidation status, telomere biology in type 2 diabetes patients (DM2). Material and methods . Into the single-movement study, 50 DM2 patients included, with no signs of cardiovascular diseases. All participants were evaluated on the carbohydrate metabolism, glycemia variablity, underwent duplex scan of carotid arteries with intima-media complex (IMC) measurement, and assessment for atherosclerotic plaques; underwent carotid-femoral pulse wave velocity measurement, endothelium dependent vasodialtion, telomere length measurement and telomerase activity (TA). Results. The parameters of vessel wall, together with the classical CVD risk factors, independently related with the various carbohydrate metabolism parameters: glycated hemoglobin, fasting glucose plasma, HOMA-IR index, C-peptide, immune reactive insulin. The GV of moderate amplitude of glycemia (MAGE) was related to IMC increase in DM2 patients. There was significant relation of telomere length (TL) not just with a chronic hyperglycemia, but with glucose level fluctuations as well, e.g. with GV standard deviation (SD), MAGE, CONGA (continuous overall net glycemic action), and such relation is higher in the analysis of “the longest” telomeres. Relation of TL and GV is not dependent on the cardiovascular risk factors, chronic inflammation and TA, but depends on fasting plasma glucose. There was no significant relation of TA with GV in DM2. There was direct correlation of GV CONGA and oxidative stress marker malonic dialdehyde, and independent negative correlation of GV (MAGE, SD, CONGA) with TL. Conclusion. GV is related with subclinical atherosclerosis (IMC) and shorter TL in DM2 patients, which is mediated by activated oxidation stress under glycemia fluctuations.

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

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

Impact of Glucose Meter Error on Glycemic Variability and Time in Target Range During Glycemic Control After Cardiovascular Surgery.

We retrospectively studied the impact of glucose meter error on the efficacy of glycemic control after cardiovascular surgery. Adult patients undergoing intravenous insulin glycemic control therapy after cardiovascular surgery, with 12-24 consecutive glucose meter measurements used to make insulin dosing decisions, had glucose values analyzed to determine glycemic variability by both standard deviation (SD) and continuous overall net glycemic action (CONGA), and percentage glucose values in target glucose range (110-150 mg/dL). Information was recorded for 70 patients during each of 2 periods, with different glucose meters used to measure glucose and dose insulin during each period but no other changes to the glycemic control protocol. Accuracy and precision of each meter were also compared using whole blood specimens from ICU patients. Glucose meter 1 (GM1) had median bias of 11 mg/dL compared to a laboratory reference method, while glucose meter 2 (GM2) had a median bias of 1 mg/dL. GM1 and GM2 differed little in precision (CV = 2.0% and 2.7%, respectively). Compared to the period when GM1 was used to make insulin dosing decisions, patients whose insulin dose was managed by GM2 demonstrated reduced glycemic variability as measured by both SD (13.7 vs 21.6 mg/dL, P < .0001) and CONGA (13.5 vs 19.4 mg/dL, P < .0001) and increased percentage glucose values in target range (74.5 vs 66.7%, P = .002). Decreasing glucose meter error (bias) was associated with decreased glycemic variability and increased percentage of values in target glucose range for patients placed on intravenous insulin therapy following cardiovascular surgery.

Evaluation of an open access software for calculating glucose variability parameters of a continuous glucose monitoring system applied at pediatric intensive care unit

BackgroundContinuous Glucose Monitoring (CGM) has become an increasingly investigated tool, especially with regards to monitoring of diabetic and critical care patients. The continuous glucose data allows the calculation of several glucose variability parameters, however, without specific application the interpretation of the results is time-consuming, utilizing extreme efforts. Our aim was to create an open access software [Glycemic Variability Analyzer Program (GVAP)], readily available to calculate the most common parameters of the glucose variability and to test its usability.MethodsThe GVAP was developed in MATLAB® 2010b environment. The calculated parameters were the following: average area above/below the target range (Avg. AUC-H/L); Percentage Spent Above/Below the Target Range (PATR/PBTR); Continuous Overall Net Glycemic Action (CONGA); Mean of Daily Differences (MODD); Mean Amplitude of Glycemic Excursions (MAGE). For verification purposes we selected 14 CGM curves of pediatric critical care patients. Medtronic® Guardian® Real-Time with Enlite® sensor was used. The reference values were obtained from Medtronic®’s own software for Avg. AUC-H/L and PATR/PBTR, from GlyCulator for MODD and CONGA, and using manual calculation for MAGE.ResultsThe Pearson and Spearman correlation coefficients were above 0.99 for all parameters. The initial execution took 30 minutes, for further analysis with the Windows® Standalone Application approximately 1 minute was needed.ConclusionsThe GVAP is a reliable open access program for analyzing different glycemic variability parameters, hence it could be a useful tool for the study of glycemic control among critically ill patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-015-0035-3) contains supplementary material, which is available to authorized users.

Does Glycemic Variability Impact Mood and Quality of Life?

Diabetes is a chronic condition that significantly impacts quality of life. Poor glycemic control is associated with more diabetes complications, depression, and worse quality of life. The impact of glycemic variability on mood and quality of life has not been studied. A descriptive exploratory design was used. Twenty-three women with type 2 diabetes wore a continuous glucose monitoring system for 72 h and completed a series of questionnaires. Measurements included (1) glycemic control shown by glycated hemoglobin and 24-h mean glucose, (2) glycemic variability shown by 24-h SD of the glucose readings, continuous overall net glycemic action (CONGA), and Fourier statistical models to generate smoothed curves to assess rate of change defined as "energy," and (3) mood (depression, anxiety, anger) and quality of life by questionnaires. Women with diabetes and co-morbid depression had higher anxiety, more anger, and lower quality of life than those without depression. Certain glycemic variability measures were associated with mood and quality of life. The 24-h SD of the glucose readings and the CONGA measures were significantly associated with health-related quality of life after adjusting for age and weight. Fourier models indicated that certain energy components were significantly associated with depression, trait anxiety, and overall quality of life. Finally, subjects with higher trait anxiety tended to have steeper glucose excursions. Data suggest that greater glycemic variability may be associated with lower quality of life and negative moods. Implications include replication of the study in a larger sample for the assessment of blood glucose fluctuations as they impact mood and quality of life.

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

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

The Effect of Glycemic Variability on Counterregulatory Hormone Responses to Hypoglycemia in Young Children and Adolescents with Type 1 Diabetes

Glycemic variability (GV) is associated with hypoglycemia and possibly diabetes-related outcomes. We hypothesized that GV and glucose excursion risk may predict counterregulatory (CR) hormone responses to hypoglycemia. This is a secondary analysis of a Diabetes Research in Children Network study containing continuous interstitial glucose monitoring records for 28 patients with type 1 diabetes between 3 to <8 or 12 to <18 years of age. GV and excursion measures, including continuous overall net glycemic action (CONGA), High Blood Glucose Index (HBGI), Low Blood Glucose Index (LBGI), and coefficient of variation (CV), were calculated 72 h prior to insulin-induced hypoglycemia. CR hormones were measured during the progressive fall in plasma glucose. CV was inversely correlated with change in glucagon concentration (r=-0.41, P=0.046), but CONGA (log-transformed for better fit of the models) was not statistically significant in univariate analysis (r=-0.34, P=0.10). Other CR hormones were not significantly associated with measures of variability. In multivariate analysis, higher CONGA, but not CV, was associated with a smaller rise in glucagon following induced hypoglycemia (estimate=-9.73, P=0.048), independent of hemoglobin A1c, duration of diabetes, and insulin dose. HBGI, LBGI, and antecedent time spent in hypoglycemia were not significantly correlated with CR response to subsequent hypoglycemia. CV and CONGA may be predictors of impaired glucagon responses to insulin-induced hypoglycemia in patients with type 1 diabetes. Further study is indicated to characterize the role of GV and glycemic excursions on the defensive response to hypoglycemia.

Impact of Glycemic and Blood Pressure Variability on Surrogate Measures of Cardiovascular Outcomes in Type 2 Diabetic Patients

OBJECTIVEThe effect of glycemic variability (GV) on cardiovascular risk has not been fully clarified in type 2 diabetes. We evaluated the effect of GV, blood pressure (BP), and oxidative stress on intima-media thickness (IMT), left ventricular mass index (LVMI), flow-mediated dilation (FMD), and sympathovagal balance (low frequency [LF]/high frequency [HF] ratio) in 26 type 2 diabetic patients (diabetes duration 4.41 ± 4.81 years; HbA1c 6.70 ± 1.25%) receiving diet and/or metformin treatment, with no hypotensive treatment or complications.RESEARCH DESIGN AND METHODSContinuous glucose monitoring (CGM) data were used to calculate mean amplitude of glycemic excursion (MAGE), continuous overall net glycemic action (CONGA)-2, mean blood glucose (MBG), mean postprandial glucose excursion (MPPGE), and incremental area under the curve (IAUC). Blood pressure (BP), circadian rhythm, and urinary 15-F2t-isoprostane (8-iso-prostaglandin F2α [PGF2α]) were also evaluated. Subjects were divided into dipper (D) and nondipper (ND) groups according to ΔBP.RESULTSIMT and LVMI were increased in ND versus D (0.77 ± 0.08 vs. 0.68 ± 0.13 [P = 0.04] and 67 ± 14 vs. 55 ± 11 [P = 0.03], respectively). MBG, MAGE, and IAUC were significantly associated with LF/HF ratio at night (r = 0.50, P = 0.01; r = 0.40, P = 0.04; r = 0.41, P = 0.04, respectively), MPPGE was negatively associated with FMD (r = −0.45, P = 0.02), and CONGA-2 was positively associated with LVMI (r = 0.55, P = 0.006). The Δsystolic BP was negatively associated with IMT (r = −0.43, P = 0.03) and with LVMI (r = −0.52, P = 0.01). Urinary 8-iso-PGF2α was positively associated with LVMI (r = 0.68 P < 0.001).CONCLUSIONSAn impaired GV and BP variability is associated with endothelial and cardiovascular damage in short-term diabetic patients with optimal metabolic control. Oxidative stress is the only independent predictor of increased LV mass and correlates with glucose and BP variability.

A Novel Approach to Continuous Glucose Analysis Utilizing Glycemic Variation

Various methodologies have been proposed for analysis of continuous glucose measurements. These methods have mainly focused on the proportion of low or high glucose readings and have not attempted to analyze other dimensions of the data obtained. This study proposes an algorithm for analysis of continuous glucose data including a novel method of assessing glycemic variability. Mean blood glucose and mean of daily differences (MODD) assessed the degree that the Continuous Glucose Monitoring System (CGMS, Medtronic MiniMed, Northridge, CA) trace was representative of the 3-month glycemic pattern. Percentages of times in low, normal, and high glucose ranges were used to assess marked glycemic excursion. Continuous overall net glycemic action (CONGA), a novel method developed by the authors, assessed intra-day glycemic variability. These methods were applied to 10 CGMS traces chosen randomly from those completed by children with type 1 diabetes from the Royal Children's Hospital, Melbourne, Victoria, Australia and 10 traces recorded by healthy volunteer controls. The healthy controls had lower values for mean blood glucose, MODD, and CONGA. Patients with diabetes had higher percentages of time spent in high and low glucose ranges. There was no overlap between the CONGA values for patients with diabetes and for controls, and the difference between controls and patients with diabetes increased markedly as the CONGA time period increased. We advocate an approach to the analysis of CGMS data based upon a hierarchy of relevant clinical questions alluding to the representative nature of the data, the amount of time spent in glycemic excursions, and the degree of glycemic variation. Integrated use of these algorithms distinguishes between various patterns of glycemic control in those with and without diabetes.