Continuous Overlapping Net Glycemic Action Research Articles

Do Metrics of Temporal Glycemic Variability Reveal Abnormal Glucose Rates of Change in Type 1 Diabetes?

Background: We aimed to identify the normal range of glucose rates of change (RoC) observed in health and assess whether existing metrics of temporal glycemic variability (GV-timing), such as mean absolute glucose change (MAG) and continuous overlapping net glycemic action (CONGA), are predictive of abnormally rapid RoC in type 1 diabetes (T1D). Methods: We identified the normal range of RoC over one-hour intervals from continuous glucose monitoring (CGM) data of healthy individuals. Rapidly rising glucose was defined as RoC values above percentiles 99% (level 1, L1) or 99.9% (level 2, L2), and rapidly falling glucose as below 1% (L1) or 0.1% (L2). The percentage of time these thresholds are exceeded in a given individual is referred to as time in fluctuation (TIF). In a separate CGM dataset of 736 T1D individuals, we calculated TIF-L1 and TIF-L2, and compared them against corresponding values of MAG and CONGA. Results: The extremum percentiles of RoC observed in health are 0.1%: −80 mg/dL/h, 1%: −50 mg/dL, 99%: +56 mg/dL/h, and 99.9%: +89 mg/dL/h. The T1D individuals spend significantly more TIF at rates exceeding these thresholds (TIF-L1: median, 16.7% [interquartile range, 12.7-21.5], TIF-L2: 5.0% [3.1-7.8]) than healthy individuals (TIF-L1: 1.4% [0.6-2.8], TIF-L2: 0.0% [0.0-0.2]). Both MAG and CONGA are highly correlated with TIF-L1 and TIF-L2 ( r > .95 in each pairwise comparison). Conclusions: Individuals with T1D spend significant time with glucose RoC exceeding those observed in health. Existing GV-timing metrics are strongly correlated with time with abnormal RoC. Incorporation of a GV-timing metric in clinical practice is recommended.

Effects of reducing free sugars on 24-hour glucose profiles and glycemic variability in subjects without diabetes.

The Western diet, especially beverages and high processed food products, is high in sugars which are associated with the development of obesity and diabetes. The reduction of refined carbohydrates including free and added sugars improves glycemic control in individuals with diabetes, but the data regarding effects in subjects without diabetes are limited. This study aimed to evaluate the effects of reducing free sugar intake on 24-h glucose profiles and glycemic variability using continuous glucose monitoring (CGM). In the randomized controlled study, 21 normal weight and overweight/obese subjects (BMI 18-40 kg/m2) without diabetes were assigned to a 4-week reduced-sugar (RS) diet or control diet after a 2-week baseline phase. During the baseline phase, all participants were advised not to change their habitual diet. During the intervention phase, RS participants were asked to avoid added sugar and white flour products, whereas participants of the control group were requested to proceed their habitual diet. Anthropometric parameters and HbA1c were assessed before and at the end of the intervention phase. Interstitial glucose was measured using continuous glucose monitoring (CGM), and the food intake was documented by dietary records for 14 consecutive days during the baseline phase and for the first 14 consecutive days during the intervention phase. Mean 24-h glucose as well as intra- and inter-day indices of glucose variability, i.e., standard deviation (SD) around the sensor glucose level, coefficient of variation in percent (CV), mean amplitude of glucose excursions (MAGE), continuous overlapping net glycemic action (CONGA), and mean absolute glucose (MAG), were calculated for the baseline and intervention phases. During the intervention, the RS group decreased the daily intake of sugar (i.e., -22.4 ± 20.2 g, -3.28 ± 3.61 EN %), total carbohydrates (-6.22 ± 6.92 EN %), and total energy intake (-216 ± 108 kcal) and increased the protein intake (+2.51 ± 1.56 EN %) compared to the baseline values, whereby this intervention-induced dietary changes differed from the control group. The RS group slightly reduced body weight (-1.58 ± 1.33 kg), BMI, total fat, and visceral fat content and increased muscle mass compared to the baseline phase, but these intervention-induced changes showed no differences in comparison with the control group. The RS diet affected neither the 24-h mean glucose levels nor intra- and inter-day indices of glucose variability, HbA1c, or diurnal glucose pattern in the within- and between-group comparisons. The dietary reduction of free sugars decreases body weight and body fat which may be associated with reduced total energy intake but does not affect the daily mean glucose and glycemic variability in individuals without diabetes. German Clinical Trials Register (DRKS); identifier: DRKS00026699.

GLYCEMIC VARIABILITY INDICATORS BY CONTINUOUS GLUCOSE MONITORING AND HYPOGLYCEMIA RISK IN CHILDREN AND YOUNG ADULTS WITH TYPE 1 DIABETES

Background: Hypoglycemia is the main barrier to optimizing insulin treatment in people with type 1 diabetes, different risk factors have been studied and one of the mechanisms involved is glycemic variability. Aims: To assess hypoglycemia risk showed by the glycemic variability metrics: coefficient of variation (CV), continuous overlapping net glycemic action (CONGA), low blood glucose index (LBGI) and lability index (LI) in a group of children, adolescents, and young adults with type 1 diabetes. Methods: A group of 31 subjects with type 1 diabetes under 25 years were evaluated, data from professional continuous glucose monitoring records were studied, glycemic variability metrics, including CV, CONGA24, LBGI, and LI, were calculated. Correlation with percentage time of hypoglycemia under 54mg% was assessed. Multiple linear regression models were generated, univariate and multivariate analysis was also performed, area under curve of glycemic variability metrics was obtain from ROC curves analysis, the optimal cutoff points were calculated. Results: The average age was 14.5 years with a range of 5 to 24 years. The mean duration of diabetes was 6.6 ± 3.7 years, and the glycated hemoglobin mean value was 8.2% ± 2.1%. The average percentage of time for hypoglycemia alert was 4.23% (0.2 to 13%), while for clinically significant hypoglycemia was 4.55% (0 to 17.1%). LBGI with R= 0.913 and CV with R= 0.735 (p < 0.0001) expressed the highest degree of correlation with percentage of time in hypoglycemia, furthermore, after multivariate analysis, they showed the highest predictive load. CV expressed an AUC of 0.97, while LBGI was 0.95, both statistically significant (p<0.0005). The cut-off point for CV of 38% had sensitivity of 93% and specificity of 74% in detection of time in hypoglycemia under 54mg/dl, and the cut-off point for LBGI of 5.4 expressed sensitivity of 87% and specificity of 94%. Conclusions: Glycemic variability metrics studied outperformed the clinical variables as indicators of risk of hypoglycemia, and those with the greatest predictive power of hypoglycemia risk were LBGI and CV above 38%.

Effects of Variability in Glycemic Indices on Longevity in Chinese Centenarians.

BackgroundLarge fluctuations in blood glucose levels greatly impact the health and life span of elderly individuals. This study describes the characteristics of variability in glycemic indices in centenarians with the aim of emphasizing the importance of glycemic variability in elderly people.MethodsWe recruited individuals from Rugao City, Jiangsu Province, China from April 2020 to May 2021. The study cohort included 60 centenarians and 60 first-generation offspring, as well as 20 randomly selected non-cohabitant control individuals aged 60–80 years. A FreeStyle Libre H (hospital version) continuous glucose monitoring (CGM) device (Abbott Ireland UK) was used to measure glycemic variability. The indices measured included the time in target glucose range (TIR), time below target glucose range (TBR), time above target glucose range (TAR), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), coefficient of variation (CV), standard deviation of blood glucose (SDBG), continuous overlapping net glycemic action (CONGA), glucose management indicator (GMI) and estimated glycated hemoglobin (eHbA1c). Logistic regression was used to analyze the association between glycemic variability and longevity.ResultsMean blood glucose (MBG), eHbA1c, GMI, mean fasting plasma glucose (M-FPG) and CONGA were lower in the centenarian group (p all < 0.05). PPGE-2 was higher in the control group than that measured in the centenarian and first-generation offspring groups (p < 0.05). There were no differences between the groups in MAGE, MODD, MAG, or TIR (p > 0.05). The risk of not achieving longevity increased with each one unit increase in MBG by 126% [2.26 (1.05–4.91)], eHbA1c by 67% [1.67 (1.03–2.72)], GMI by 568% [6.68 (1.11–40.30)], M-FPG by 365% [4.65 (1.57–13.75)], M-PPG1h by 98% [1.98 (1.18–3.31)], CONGA1 by 102% [2.02 (1.01–4.06)], Li by 200% [3.00 (1.04–8.61)], and PPGE-2 by 150% [2.50 (1.39–4.50)]. However, the risk of achieving longevity decreased with each unit increase of LBGI by 53% [0.47 (0.28–0.80)], ADRR by 60% [0.40 (0.18–0.86)], and TBR by 11% [0.89 (0.80–0.98)].ConclusionFluctuation in blood glucose levels in centenarians is relatively small. Maintaining an average blood glucose level and keeping blood glucose fluctuations in the normal range is conducive to longevity.

710-P: In Patients with Type 2 Diabetes Mellitus (T2DM) , Continuous Glucose Monitoring System (CGMS) Usage Does Not Alter Multiple Measures of Glycemic Variability (GV)

Background: The RCT data on CGMS and GV in patients with T2DM have used limited measure of GV metrics. In patients with T2DM on CGMS, we examined whether the use of blinded or unblinded CGMS will affect their GV across a broad swatch of metrics. Methods: A randomized control study involving 40 subjects with T2DM was done [50% females, mean (SD) age 59 years (12.5) , BMI 34.kg/m2 (8.1) , mean (SD) screening HbA1c 7.59% (1.2) ]. Patients were randomized (1:1) to either 1 unblinded (Freestyle Libre) +1 blinded (Freestyle Libre Pro) CGMs or 2 blinded CGMS (Freestyle Libre Pro) for 16 weeks. Sensors were replaced every 2 weeks with an in-person visit. Multiple GV metrics were calculated from the blinded CGM at baseline (Week 0-Week2) and end-intervention (Week 14-16) . Participants randomized to the Unblinded/Blinded CGM group were instructed to use the CGM as they wished. Participants randomized to the Blinded CGM group were instructed to continue checking their fingerstick glucose as usual. Measured GV metrics included the following: mean glucose, standard deviation (SD) , and coefficient of variation (CV) , continuous overlapping net glycemic action (CONGA) , liability Index, J-index, low and high blood glucose index (LBGI/HBGI) , glycemic risk assessment diabetes equation (GRADE) , mean of daily differences (MODD) , average daily risk range (ADRR) , mean amplitude of glycemic excursions (MAGE) , mean of daily differences (MODD) , Area Under Curve (AUC) , time spent in range (TIR) , Glucose Management Indicator (GMI) , number of episodes below 55 and glycemic variability percentage (GVP) . Results: In this study, we found that was no significant difference in all GV metrics between the blinded/ blinded CGMS group and unblinded/blinded CGMS group at baseline and end intervention. Conclusion: The novelty of this study is the extensive analysis of GV across multiple metrics in patients with T2DM on CGMS and CGMS usage did not significantly alter GV. Disclosure Y.Lee: None. A.Mehfooz: None. Q.Wang: None. L.S.Chow: Other Relationship; Dexcom, Inc.

699-P: Time-Restricted Eating Did Not Alter Glycemic Variability in Humans Who Are Overweight and without Diabetes

Background: Time-restricted eating (TRE) is increasingly popular for weight loss, yet its effects on glycemic measures remain unclear. Using continuous glucose monitor (CGM) worn for 2 weeks at baseline and end-intervention, we hypothesized that TRE may reduce glycemic variability compared to unrestricted eating (non-TRE) in patients who are overweight and without diabetes. Method: Participants (17 women and 3 men; mean [SD] age: 45.5 [12.1] years; BMI 34.1 [7.5] kg/m2) without self-reported diabetes (HbA1c 5.5 [0.4]) who had prolonged eating window (15.4 [0.9] hours) were randomized to a 12 week intervention of either TRE (n=11: 8-hour window, unrestricted eating within window) versus non-TRE (n=9: unrestricted eating) . Each participant wore a blinded CGM (Freestyle Libre Pro) for 2 weeks prior to randomization and at end-intervention (week 11-12) . At each time point, multiple measures of glycemic variability were calculated from the raw CGM data. This is a secondary analysis of a previously published study where 12 weeks of TRE did not alter HbA1c. Result: Between the TRE and non-TRE groups in the pre and end-intervention periods, there was no statistical difference in any of the calculated glycemic variability measures: including, time in range (TIR) , standard deviation (SD) , coefficient of variation (CV) , continuous overlapping net glycemic action (CONGA) , liability, Index, J-index, low and high blood glucose index (LBGI/HBGI) , glycemic risk assessment diabetes equation (GRADE) , mean of daily differences (MODD) , average daily risk range (ADRR) , mean amplitude of glycemic excursions (MAGE) , mean of daily differences (MODD) , Area Under Curve (AUC) , and Glucose Management Indicator (GMI) . Conclusion: In patients who are overweight without diabetes, TRE did not alter CGM-measured glycemic variability. Further studies regarding TRE effects on glycemic variability should focus on participants with diabetes. Disclosure A.Mehfooz: None. Y.Lee: None. Q.Wang: None. L.S.Chow: Other Relationship; Dexcom, Inc.

Glucose variability in subjects with normal glucose tolerance: Relations with body composition, insulin secretion and sensitivity

Background and aimsTo estimate the determinants of glucose variability (GV) in young and middle-aged non-obese subjects with normal glucose tolerance (NGT) we assessed relations between GV parameters, body composition, insulin secretion and sensitivity indices. MethodsThirty individuals with normal body mass index (BMI) and twenty overweight subjects were included. 24-hour mean glucose, time in range, time above range (TAR), time below range (TBR), standard deviation (SD), coefficient of variation (CV), mean amplitude of glucose excursions (MAGE), continuous overlapping net glycemic action (CONGA), J-index, lability index (LI), mean absolute glucose (MAG), M-value, high blood glucose index (HBGI), low blood glucose index (LBGI) were derived from continuous glucose monitoring. Body composition was assessed by DEXA. Insulin secretion and sensitivity was estimated by HOMA-IR and HOMA-B scores. ResultsOverweight subjects demonstrated higher mean glucose, CONGA, J-index and lower TBR, M-value and LBGI values. Mean glucose correlated positively with total, trunk, gynoid and android fat mass, while M-value and LBGI demonstrated negative correlations with these parameters. In multiple stepwise regression analysis, android fat mass was a predictor of mean glucose, CONGA, J-index, SD and MAGE, gynoid fat mass predicted J-index only, and total fat mass was associated inversely with MAG. Fasting insulin was a predictor of TAR, SD, CV, MAGE, MAG, LI and HBGI. HOMA-B was associated with CONGA, M-value and LBGI. ConclusionIn non-obese subjects with NGT mean glucose and GV parameters are related to fat mass and fat distribution. These relations can be mediated through insulin secretion and sensitivity.

135-OR: Impaired Awareness of Hypoglycemia Is Associated with Residual Beta-Cell Function and Glucose Variability Parameters in Patients with Type 1 Diabetes

The Aim: to evaluate the relationships between impaired awareness of hypoglycemia (IAH), residual beta cell function and short-term glucose variability (GV) in patients with type 1 diabetes (T1D). Materials and Methods: We observed 400 patients, 145 M/255 F, age 18-85 years (median - 36 years), diabetes duration 0.5-52 years (median - 16 years), HbA1c 4.7-15.1% (median - 8%). The Clarke's questionnaire was used for IAH recognition. Beta cell function was estimated by fasting and 2-h postprandial C-peptide levels. Day-time (6.00-23.59), nocturnal (0.00-5.59) and 24-h Time In target Range (TIR), Time Above target Range (TAR), Time Below target Range (TBR), and GV parameters, including Standard Deviation (SD), Coefficient of Variation (CV), Mean Amplitude of Glucose Excursions (MAGE), 2-h Continuous Overlapping Net Glycemic Action (CONGA), Lability Index (LI), J-index, Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), M-value and Mean Absolute Glucose (MAG), were calculated from short-term (3-7 days) continuous glucose monitoring (CGM). Results: In our cohort, IAH was recognized in 163 (40.8%) individuals. Patients with IAH, as compared to those without, demonstrated longer diabetes duration (P&amp;lt;0.0001), lower C-peptide postprandial level and increment (p=0.02 and p=0.0003 respectively) and increased day-time and nocturnal mean glucose, SD, CONGA, J-index, HBGI and M-value (all P≤0.02). Nocturnal MAGE, LI and LBGI, as well as 24-h mean glucose, SD, CV, MAGE, CONGA, J-index, HBGI, and M-value were also higher in patients with IAH (all P≤0.01). No differences in HbA1c, TIR, TAR and TBR were found between the groups. Day-time, nocturnal and 24-h SD, CV, LI, J-index, and HBGI, as well as day-time and 24-h MAGE, correlated negatively with postprandial C-peptide. Conclusion: In patients with T1D, IAH is associated with low (or zero) residual beta cell function. This relationship could be mediated via enhanced GV. Disclosure V. Klimontov: Advisory Panel; Self; Boehringer Ingelheim International GmbH, Sanofi, Research Support; Self; Boehringer Ingelheim International GmbH, Speaker’s Bureau; Self; AstraZeneca, Medtronic. J. F. Semenova: None. A. I. Korbut: None. Funding Russian Science Foundation (20-15-00057)

Changes in Free-Living Glycemic Profiles after 12 Months of Lifestyle Intervention in Children with Overweight and with Obesity.

Previous studies demonstrated that hyperglycemic glucose concentrations are observed in children that are overweight or have obesity. The aim of this study was to evaluate the effect of a 12 month lifestyle intervention on free-living glycemic profiles in children that were overweight or had obesity, and the association of the alterations with changes in cardiovascular risk parameters. BMI z-score, free-living glycemic profiles, continuous overlapping net glycemic action (CONGA), and cardiovascular parameters were evaluated before and after a multidisciplinary lifestyle intervention, in 33 non-diabetic children that were overweight or had obesity. In children with a decrease in BMI z-score, the duration which glucose concentrations were above the high-normal threshold (6.7 mmol/L) and the glycemic variability decreased significantly. In these children, a decrease in median sensor glucose was associated with decreases in LDL-cholesterol, and systolic and diastolic blood pressure z-score. A decrease in BMI z-score was associated with a decrease in CONGA1, 2, and 4. In conclusion, the glycemic profiles in free-living conditions in children that were overweight improved in children with a decrease in BMI z-score after lifestyle intervention. In those children, changes in median sensor glucose concentrations were associated with changes in LDL-cholesterol and blood pressure z-scores. These results suggest that glucose homeostasis can improve after one year of lifestyle intervention and that these improvements are associated with improvements in cardiovascular health parameters.

Impact of Carbohydrate on Glucose Variability in Patients with Type 1 Diabetes Assessed Through Professional Continuous Glucose Monitoring: A Retrospective Study

IntroductionThe aim of this study was to objectively analyze the correlation between dietary components and blood glucose variation by means of continuous glucose monitoring (CGM).MethodsPatients with type 1 diabetes mellitus (T1DM) who received CGM to manage their blood glucose levels were enrolled into the study, and the components of their total caloric intake were analyzed. Glycemic variation parameters were calculated, and dietary components, including percentages of carbohydrate, protein and fat in the total dietary intake, were analyzed by a dietitian. The interaction between parameters of glycemic variability and dietary components was analyzed.ResultsSixty-one patients with T1DM (33 females, 28 males) were enrolled. The mean age of the participants was 34.7 years, and the average duration of diabetes was 14 years. Glycated hemoglobin before CGM was 8.54%. Participants with a carbohydrate intake that accounted for < 50% of their total caloric intake had a longer DM duration and a higher protein and fat intake than did those with a carbohydrate intake that accounted for ≥ 50% of total caloric intake, but there was no between-group difference in total caloric intake per day. The group with a carbohydrate intake that accounted for < 50% of their total caloric intake also had lower nocturnal continuous overlapping net glycemic action (CONGA) 1, − 2 and − 4 values. The percentage of protein intake had a slightly negative correlation with mean amplitude of glycemic excursions (MAGE) (r = − 0.286, p < 0.05) and a moderately negative correlation with coefficient of variation (CV) (r = 0.289, p < 0.05). One additional percentage of protein calories of total calories per day decreased the MAGE to 4.25 mg/dL and CV to 0.012 (p < 0.05). The optimal dietary protein percentage for MAGE < 140 mg/dL was 15.13%. The performance of predictive models revealed the beneficial effect of adequate carbohydrate intake on glucose variation when combined with protein consumption.ConclusionsAdequate carbohydrate consumption—but not more than half the daily total calories—combined with protein calories that amount to approximately 15% of the daily caloric intake is important for glucose stability and beneficial for patients with T1DM.Electronic supplementary materialThe online version of this article (10.1007/s13300-019-00707-x) contains supplementary material, which is available to authorized users.

Patterns of Glycemic Variability During a Diabetes Self-Management Educational Program.

Background: Variations in blood glucose levels over a given time interval is termed as glycemic variability (GV). Higher GV is associated with higher diabetes-related complications. The current study was done with the aim of detecting the sensitivity of various GV indices among individuals with type 2 diabetes mellitus of different glycemic control status. Methods: We performed a longitudinal study among individuals with type 2 diabetes mellitus (T2DM) who were participating in a two-week diabetes self-management education (DSME) program. Participants were categorized by their HbA1c as poor (≥8%), acceptable (7%–8%), and optimal control (<7%). Continuous glucose monitoring (CGM) sensors recorded interstitial glucose every 15 min from day 1. The evaluated GV measures include standard deviation (SD), coefficient of variation (CV), mean amplitude of glycemic excursion (MAGE), continuous overlapping net glycemic action (CONGA), mean of daily difference for inter-day variation (MODD), high blood glucose index (HBGI), and low blood glucose index (LBGI). Results: A total of 41 study participants with 46347 CGM values were available for analysis. Of 41 participants, 20 (48.7%) were in the poor, 10 (24.3%) in the acceptable, and 11 (26.8%) in the optimal control group. The GV indices (SD; CV; MODD; MAGE; CONGA; HBGI) of poorly controlled (77.43; 38.02; 45.82; 216.63; 14.10; 16.62) were higher than acceptable (50.02; 39.32; 30.79; 138.01; 8.87; 5.56) and optimal (34.15; 29.46; 24.56; 126.15; 8.67; 3.13) control group. Glycemic variability was reduced in the poorly and acceptably controlled groups by the end of the 2-week period. There was a rise in LBGI in the optimally controlled group, indicating pitfalls of tight glycemic control. Conclusion: Indices of glycemic variability are useful complements, and changes in it can be demonstrated within short periods.

Glycemic Variability from CGM Correlates with Indices of Glucose Metabolism in Healthy Obesity but Not in Type 2 Diabetes

We evaluated the relationship between measurements of beta cell function and insulin resistance assessed from OGTT and 2-step glucose clamp data with indices of glucose variability (GV) obtained with a continuous glucose monitor (CGM) in obese subjects with and without type 2 diabetes (T2D). 17 healthy obese (OB) (FPG 91 ± 4.9mg/dl and A1c 5.2 ± 0.4%) and 8 age- and weight-matched participants with T2D treated with diet and/or metformin (FPG 131.5 ± 42.8 mg/dl and A1c 6.8 ± 0.8%) wore CGM (Dexcom®) for up to 5 days. EasyGV® software was used to calculate indices of GV: standard deviation (SD), mean amplitude of glycemic excursions (MAGE), J-Index, continuous overlapping net glycemic action (CONGA), and Glycemic Risk Assessment in Diabetes Equation (GRADE). Indices of glucose metabolism were determined with a 2 hour OGTT and a two-step hyperglycemic-euglycemic clamp. Insulin levels during the hyperglycemic-step of clamp (116 ± 126 vs. 40 ± 23 mU/mL) were equal in both groups, while glucose infusion rate (GIR) (7.7 ± 3.1 vs. 3.9 ± 2.4 mg/kg/minutes) during euglycemia was as expected significantly lower in the T2D subjects compared to OB subjects. GV indices (SD, CONGA, J-Index, GRADE) were statistically lower in OB vs. T2D (P&amp;lt;0.05). Insulin Sensitivity Index (ISI0-120) and the insulin secretion-sensitivity index-2 (ISSI-2) were also lower in obese than in T2D subjects (P&amp;lt;0.001). CGM indices (mean, CONGA, J-index, and GRADE) correlated with incremental AUC of insulin during the OGTT, but not for glucose; as well as insulin levels during the hyperglycemic-step (r=0.740, P&amp;lt;0.01), and GIR during the euglycemic step of the clamp (r= -0.733, P&amp;lt;0.01). CGM measures of GV (CONGA, J-Index, and GRADE) were lower in obese compared to T2D subjects. These results suggest that GV indices are surrogate measurements of beta cell function and insulin action in obese subjects, and may help determine progression to T2D. Disclosure A. Elshafie: None. W. Lam: None. L. Parikh: None. E. Sanchez Rangel: None. C.P. Schmidt: None. J. Hwang: None. C.W. Yeckel: None. R. Sherwin: Other Relationship; Self; QuintilesIMS, MannKind Corporation. Research Support; Self; Regeneron Pharmaceuticals, Inc.. Other Relationship; Self; ICON plc. R. Belfort-DeAguiar: Research Support; Self; GlaxoSmithKline plc..

Clinical Implications of Lipohypertrophy Among People with Type 1 Diabetes in India.

Lipohypertrophy (LH) at insulin injection sites is a common but preventable complication in type 1 diabetes mellitus (T1DM). We evaluated the prevalence, contributing risk factors, and consequences of LH, specifically the glycemic variability (GV) among T1DM patients. This is a cross-sectional study conducted at a tertiary care center in India, wherein 139 subjects with T1DM were randomly selected and evaluated for the presence of LH through visual and palpation examinations. Demography, anthropometry, and injecting practices were evaluated using a validated questionnaire and their effect on LH was determined. Subsequently, the effect of LH on GV and unexplained hypoglycemia (UH) was studied. Mean glucose, mean amplitude of glycemic excursions (MAGEs), and continuous overlapping net glycemic action (CONGA) were assessed in a subset of patients who injected insulin alternately in LH and non-LH sites. The overall prevalence of LH was 69.8%, and was significantly higher in adults than in children (P = 0.038). Improper rotation of sites (P < 0.0001) and insulin syringe reusage for more than five times (P = 0.009) significantly increase the risk of LH. The presence of LH has a significant effect on GV and UH with adjusted odds ratios of 17.65 (P < 0.0001) and 28.02 (P < 0.0001), respectively. Ambulatory glucose monitoring on a subset of patients confirmed that the mean glucose, MAGE, and CONGA were higher when subjects injected insulin at LH sites than at non-LH sites. Improper rotation of sites and reuse of needles are the leading causes of LH in Indian T1DM patients, which, in turn, significantly increases the risk of GV and UH.

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.

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

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

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.

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.

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 Fluctuations during Gestation: An Additional Tool for Monitoring Pregnancy Complicated by Diabetes.

Continuous glucose monitoring (CGM) gives a unique insight into magnitude and duration of daily glucose fluctuations. Limited data are available on glucose variability (GV) in pregnancy. We aimed to assess GV in healthy pregnant women and cases of type 1 diabetes mellitus or gestational diabetes (GDM) and its possible association with HbA1c. CGM was performed in 50 pregnant women (20 type 1, 20 GDM, and 10 healthy controls) in all three trimesters of pregnancy. We calculated mean amplitude of glycemic excursions (MAGE), standard deviation (SD), interquartile range (IQR), and continuous overlapping net glycemic action (CONGA), as parameters of GV. The high blood glycemic index (HBGI) and low blood glycemic index (LBGI) were also measured as indicators of hyperhypoglycemic risk. Women with type 1 diabetes showed higher GV, with a 2-fold higher risk of hyperglycemic spikes during the day, than healthy pregnant women or GDM ones. GDM women had only slightly higher GV parameters than healthy controls. HbA1c did not correlate with GV indicators in type 1 diabetes or GDM pregnancies. We provided new evidence of the importance of certain GV indicators in pregnant women with GDM or type 1 diabetes and recommended the use of CGM specifically in these populations.

Glucose Variability in Diabetic Pregnancy

Fetal overgrowth is the most important complication of gestational (GDM) and pregestational diabetes mellitus. We correlated maternal glucose profiles, as detected by continuous glucose monitoring (CGM), with fetal growth parameters for 80 pregnant women (32 with type 1 diabetes, 31 with GDM, and 17 healthy controls). Glucose profiles were monitored in the first, second, and third trimesters of pregnancy for type 1 diabetes women and in the second and third trimesters for GDM women and controls. To analyze glycemic variability, we considered the mean amplitude of glycemic excursion, mean glycemia, the continuous overlapping net glycemic action (CONGA), the SD, the High Blood Glucose Index (HBGI), the Low Blood Glucose Index, and the interquartile range (IQR). Mean age was the same for the three groups. Prepregnancy body mass index was higher for the women with diabetes (GDM and type 1) than for controls. The newborn's mean birth weight and ponderal index were higher, although not significantly so, for the women with diabetes than for controls. For the type 1 diabetes patients, ponderal index correlated with the HBGI in the first trimester, CONGA1 and IQR in the second, and mean glycemia and SD in the third. For GDM patients, ponderal index correlated with mean glycemia and the HBGI in the second trimester. Fetal exposure to glycemic variability and hyperglycemia seems to be important in determining fetal overgrowth in pregnant women with diabetes. Optimal glucose control and less glucose variability are needed as early as possible in both type 1 diabetes and GDM patients to ensure normal fetal growth.