Low Blood Glucose Index Research Articles

A Novel CGM Metric-Gradient and Combining Mean Sensor Glucose Enable to Improve the Prediction of Nocturnal Hypoglycemic Events in Patients with Diabetes.

Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, and it is often asymptomatic. A novel CGM metric-gradient was proposed in this paper, and a method of combining mean sensor glucose (MSG) and gradient was presented for the prediction of nocturnal hypoglycemia. For this purpose, the data from continuous glucose monitoring (CGM) encompassing 1,921 patients with diabetes were analyzed, and a total of 302 nocturnal hypoglycemic events were recorded. The MSG and gradient values were calculated, respectively, and then combined as a new metric (i.e., MSG+gradient). In addition, the prediction was conducted by four algorithms, namely, logistic regression, support vector machine, random forest, and long short-term memory. The results revealed that the gradient of CGM showed a downward trend before hypoglycemic events happened. Additionally, the results indicated that the specificity and sensitivity based on the proposed method were better than the conventional metrics of low blood glucose index (LBGI), coefficient of variation (CV), mean absolute glucose (MAG), lability index (LI), etc., and the complex metrics of MSG+LBGI, MSG+CV, MSG+MAG, and MSG+LI, etc. Specifically, the specificity and sensitivity were greater than 96.07% and 96.03% at the prediction horizon of 15 minutes and greater than 87.79% and 90.07% at the prediction horizon of 30 minutes when the proposed method was adopted to predict nocturnal hypoglycemic events in the aforementioned four algorithms. Therefore, the proposed method of combining MSG and gradient may enable to improve the prediction of nocturnal hypoglycemic events. Future studies are warranted to confirm the validity of this metric.

Intermittently Scanned Glucose Values for Continuous Monitoring: Cross-Sectional Analysis of Glycemic Control and Hypoglycemia in 1809 Children and Adolescents with Type 1 Diabetes.

Background and Objective: Intermittent scanning continuous glucose monitoring (iscCGM) is increasingly used for glycemic monitoring in diabetes care. In this cross-sectional real-world analysis, iscCGM data were compared to traditional parameters of glycemic control in young people with type 1 diabetes. Methods: Using the DPV registry, most recent data from children and adolescents aged <18 years with uploaded iscCGM sensor profiles with at least 14 days of data and a > 50% completeness were evaluated using recommended parameters of sensor metrics. Analysis was performed stratified by age group, glycemic control, and type of therapy; data were taken from DPV data pool in February 2020. Results: Glucose sensor profiles and clinical data from 1809 individuals (mean age 13.4 years, 53% male, and mean diabetes duration 5.02 years) were evaluated in this study. More than 50% of this population (n = 965) reached the current German treatment target of hemoglobin A1c (HbA1c) <7.5%. In this target, the mean scanning frequency was higher than in groups with HbA1c >7.5 or >8.0% (12.0 vs. 10.2 vs 7.6 times per day). The group of preschool children had the highest frequency of scanning (16.6 vs. 13.3 times per day in school kids and 7.9 in adolescents), the lowest HbA1c level, and the lowest risk for hypoglycemia (low blood glucose index 0.8 vs. 1.0 vs 1.2). Conclusion: Real-world data will help to determine the value of iscCGM to improve clinical and patient-related outcomes in pediatric diabetology. Not only the use of a device but also the intensity of use seems to have a high and direct impact on glycemic control.

305-OR: Glycemic Variability and Progression of CKD: PERL Substudy

To test the hypothesis that glucose variability may impact the progression of kidney disease in type 1 diabetes (T1D), we studied a subgroup of the Preventing Early Renal Loss (PERL) study population with T1D and mild to moderate diabetic kidney disease, using blinded continuous glucose monitoring (CGM) for up to 14 days for each wear period. We studied 175 participants wearing 366 blinded FreeStyle Libre CGMs on one to five occasions between weeks 96 and 164 of the follow-up in the parent study. The mean age of the population was 52.2 ± 10.9 (mean ± SD) years, mean duration of T1D was 36.1 ± 12.2 years. Baseline BMI, A1C, and iothalamate GFR (iGFR) were 29.9 ± 6.3 kg/m2, 8.2 ± 1.2%, and 67.5 ± 17.2 ml/min/1.73m2 respectively. During the study, mean glucose (MG) and coefficient of variation glucose (CV) were 174.5 ± 41.4 mg/dL and 44.5± 8.8% respectively. Mean amplitude of glycemic excursions (MAGE) was 146.0 ± 43.9 mg/dL while time-in-range (TIR) was 50.5 ± 16.1%. A 10% increase in the mean CV was associated with a 1.4 ml/min/1.73m2 decrease in iGFR at the end of the study [95%CI (-3.33, 0.44); p=0.13] while a 10 mg/dL increase in MAGE was associated with a 0.01 ml/min/1.73m2 decrease [95%CI (-0.41, 0.38); p=0.95]. For those with CV &amp;gt;36, iGFR was 2.56 ml/min/1.73m2 lower compared with those with CV &amp;lt;36 [95%CI (-7.44, 2.31); p=0.30]. Low blood glucose index, high blood glucose index, MG, and TIR were not significantly associated with iGFR. Given the non-significant associations between CGM metrics and iGFR, we conclude in this study of a population with T1D and mild to moderate kidney disease that there is no evidence that glycemic variability or glucose control impacts the rate of loss of iGFR. Limitations of the study include the observational study design, the relatively small sample size, the variability of how frequently CGM data were collected, and the short duration of observation. A larger prospective study using real-time CGM to improve glycemic parameters and reduce glycemic variability is required to answer this question definitively. Disclosure J.B. McGill: Advisory Panel; Self; Aegerion Pharmaceuticals, Bayer AG, Lilly Diabetes, Novo Nordisk A/S. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc. Research Support; Self; Medtronic, Novo Nordisk Inc., Sanofi. Speaker’s Bureau; Self; Aegerion Pharmaceuticals, Dexcom, Inc., Janssen Pharmaceuticals, Inc. I.B. Hirsch: Consultant; Self; Abbott, Bigfoot Biomedical, Roche Diabetes Care. Research Support; Self; Medtronic, Omnipod. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. A. Galecki: None. K. Ruedy: None. C. Wu: None. A. Doria: Research Support; Self; Sanofi. Funding The Leona M. and Harry B. Helmsley Charitable Trust (2018PG-TD1014); National Institute of Diabetes and Digestive and Kidney Diseases (R03DK094484, R34DK097808, UC4DK101108); JDRF (17-2012-377)

0927 Variations in Sleep and Glucose in Adolescents with Type 1 Diabetes

Abstract Introduction The association between short sleep duration and poorer glycemic control in adolescents ages 10-16 with type 1 diabetes (T1D) is well established. Researchers have used cross-sectional, between-subjects’ methods, with limited focus on the potential intraindividual variation among these variables. The purpose of this analysis was to examine the within person associations between glucose variability indices (J index, low/high blood glucose index, time in range) and sleep characteristics (bedtime, waketime, total sleep time, sleep efficiency, wake after sleep onset [WASO], awakenings, and sleep fragmentation index) in adolescents with T1D. Methods Adolescents monitored their sleep and glucose patterns concurrently for 3-7 days with a wrist actigraph on their non-dominant wrist and either their own continuous glucose monitor (CGM) or a provided blinded CGM. General linear mixed models (GLMM) were used to determine within-person and day level associations. Results The sample included 38 adolescents (M age 13.4±1.8; 37.8% male; M A1C 8.2±1.2%). Average glucose levels were controlled in all GLMMs. Adolescents had earlier waketimes on days when more time was spent in hypoglycemia &amp;lt;70mg/dL (β=-0.15, p&amp;lt;0.001). At the person level, adolescents had greater WASO with more % time spent in severe hypoglycemia &amp;lt;54mg/dL with more severe low blood glucose indices (β=0.35, p&amp;lt;0.01 and β=0.34, p&amp;lt;0.01 respectively). At the daily level, adolescents had greater WASO (β=0.20, p=0.01) and more awakenings (β=0.16, p=0.04) on the days they had more overall glucose variability (J index) and more severe high blood glucose indices (β=0.17, p=0.04), but were less likely to have more % time in hypoglycemia (β=-0.15, p=0.02). Conclusion Glucose variability was positively associated with poor sleep (e.g., WASO and awakenings) in adolescents with T1D both at the daily and intraindividual level. Monitoring over a longer period of time in subsequent studies would allow researchers to determine the within person associations between habitual short sleep duration and glucose variability. Support NINR T32NR0008346 &amp; P20NR014126, Medtronic MiniMed provided CGMs at a discounted rate for the study.

Low Blood Glucose Index and Hypoglycaemia Risk: Insulin Glargine 300U/mL Versus Insulin Glargine 100 U/mL in Type 2 Diabetes.

IntroductionWe examined differences in hypoglycaemia risk between insulin glargine 300 U/mL (Gla-300) and insulin glargine 100 U/mL (Gla-100) in individuals with type 2 diabetes (T2DM) using the low blood glucose index (LBGI).MethodsDaily profiles of self-monitored plasma glucose (SMPG) from the EDITION 2, EDITION 3 and SENIOR treat-to-target trials of Gla-300 versus Gla-100 were used to compute the LBGI, which is an established metric of hypoglycaemia risk. The analysis also examined documented (blood glucose readings < 3.0 mmol/L [54 mg/dL]) symptomatic hypoglycaemia (DSH).ResultsOverall LBGI in EDITION 2 and SENIOR and night-time LBGI in all three trials were significantly (p < 0.05) lower with Gla-300 versus Gla-100. The largest differences between Gla-300 and Gla-100 were observed during the night. In all three trials, individual LBGI results correlated with the observed number of DSH episodes per participant (EDITION 2 [r = 0.35, p < 0.001]; EDITION 3 [r = 0.26, p < 0.001]; SENIOR [r = 0.30, p < 0.001]). Participants at moderate risk of experiencing hypoglycaemia (defined as LBGI > 1.1) reported 4- to 8-fold more frequent DSH events than those at minimal risk (LBGI ≤ 1.1) (p ≤ 0.009).ConclusionsThe LBGI identified individuals with T2DM at risk for hypoglycaemia using SMPG data and correlated with the number of DSH events. Using the LBGI metric, a lower risk of hypoglycaemia with Gla-300 than Gla-100 was observed in all three trials. The finding that differences in LBGI are greater at night is consistent with previously published differences in the pharmacokinetic profiles of Gla-300 and Gla-100, which provides the physiological foundation for the presented results.Electronic Supplementary MaterialThe online version of this article (10.1007/s13300-020-00808-y) contains supplementary material, which is available to authorized users.

Assessment of Glucose Control Metrics by Discriminant Ratio

Objective: Increasing use of continuous glucose monitoring (CGM) data has created an array of glucose metrics for glucose variability, temporal patterns, and times in ranges. However, a gold standard metric has not been defined. We assess the performance of multiple glucose metrics to determine their ability to detect intra- and interperson variability to determine a set of recommended metrics. Methods: The Juvenile Diabetes Research Foundation data set, a randomized controlled study of CGM and self-monitored blood glucose conducted in children and adults with type 1 diabetes (T1D), was used. To determine the ability of the evaluated glycemic metrics to discriminate between different subjects and attenuate the effect of within-subject variation, the discriminant ratio was calculated and compared for each metric. Then, the findings were confirmed using data from two other recent randomized clinical trials. Results: Mean absolute glucose (MAG) has the highest discriminant ratio value (2.98 [95% confidence interval {CI} 1.64-3.67]). In addition, low blood glucose index and index of glycemic control performed well (1.93 [95% CI 1.15-3.44] and 1.92 [95% CI 1.27-2.93], respectively). For percentage times in glucose target ranges, the optimal discriminator was percentage time in glucose target 70-180 mg/dL. Conclusions: MAG is the optimal index to differentiate glucose variability in people with T1D, and may be a complementary therapeutic monitoring tool in addition to glycated hemoglobin and a measure of hypoglycemia. Percentage time in glucose target 70-180 mg/dL is the optimal percentage time in range to report.

Value of Capillary Glucose Profiles in Assessing Risk of Nocturnal Hypoglycemia in Type1 Diabetes Based on Continuous Glucose Monitoring.

IntroductionThis study aimed to evaluate the occurrence of nocturnal hypoglycemia in type 1 diabetes (T1D) based on continuous glucose monitoring (CGM), and to explore the value of capillary glucose profiles in assessing the risk of nocturnal hypoglycemia. The study also intended to develop a predictive model to identify people with high risk of nocturnal hypoglycemia.MethodsA total of 169 participants with T1D received 3 days of blinded CGM; meanwhile, their self-monitoring blood glucose (SMBG) profiles were recorded. Logistic regression analyses were used to evaluate contributory factors of nocturnal hypoglycemia. Potential indicators were estimated using area under receiver operator curve (AUC) analyses.ResultsDuring the retrospective CGM period, 95 (56.2%) participants with T1D reported 238 events of hypoglycemia, and 69 (29.0%) of these episodes occurred during the nighttime. Increased risk of nocturnal hypoglycemia correlated with lower HbA1c, glycated albumin, and mean blood glucose (OR = 0.790, 0.940, 0.651, respectively; P < 0.05) and higher standard deviation, mean amplitude of glycemic excursions, and low blood glucose index (OR = 1.463, 1.168, 4.035, respectively; P < 0.05) after adjustment for age and duration. Of the daily SMBG profiles, fasting blood glucose (OR = 0.643, P = 0.001) and blood glucose at bedtime (OR = 0.851, P = 0.037) were associated with the occurrence of nocturnal hypoglycemia. The BGn model, which was derived from the variation of capillary glucose, could discriminate individuals with increased risk of nocturnal hypoglycemia (AUC = 0.774).ConclusionsNocturnal hypoglycemia constitutes nearly one-third of hypoglycemic events in people with T1D. Strict glycemic control and great fluctuation of glucose are potential contributory factors. Daily SMBG profiles and the BGn model could help assess the risk of nocturnal hypoglycemia in T1D, which may support further development of preventive strategies.

The Use of a Smart Bolus Calculator Informed by Real-time Insulin Sensitivity Assessments Reduces Postprandial Hypoglycemia Following an Aerobic Exercise Session in Individuals With Type 1 Diabetes.

Insulin dosing in type 1 diabetes (T1D) is oftentimes complicated by fluctuating insulin requirements driven by metabolic and psychobehavioral factors impacting individuals' insulin sensitivity (IS). In this context, smart bolus calculators that automatically tailor prandial insulin dosing to the metabolic state of a person can improve glucose management in T1D. Fifteen adults with T1D using continuous glucose monitors (CGMs) and insulin pumps completed two 24-h admissions in a hotel setting. During the admissions, participants engaged in an early afternoon 45-min aerobic exercise session, after which they received a standardized dinner meal. The dinner bolus was computed using a standard bolus calculator or smart bolus calculator informed by real-time IS estimates. Glucose control was assessed in the 4 h following dinner using CGMs and was compared between the two admissions. The IS-informed bolus calculator allowed for a reduction in postprandial hypoglycemia as quantified by the low blood glucose index (2.02 vs. 3.31, P = 0.006) and percent time <70 mg/dL (8.48% vs. 15.18%, P = 0.049), without increasing hyperglycemia (high blood glucose index: 3.13 vs. 2.09, P = 0.075; percent time >180 mg/dL: 13.24% vs. 10.42%, P = 0.5; percent time >250 mg/dL: 2.08% vs. 1.19%, P = 0.317). In addition, the number of hypoglycemia rescue treatments was reduced from 12 to 7 with the use of the system. The study shows that the proposed IS-informed bolus calculator is safe and feasible in adults with T1D, appropriately reducing postprandial hypoglycemia following an exercise-induced IS increase.

Comparison of CGM-Derived Measures of Glycemic Variability Between Pancreatogenic Diabetes and Type 2 Diabetes Mellitus.

To compare glycemic variability (GV) indices between patients with fibrocalculous pancreatic diabetes (FCPD) and type 2 diabetes mellitus (T2D) using continuous glucose monitoring (CGM). We measured GV indices using CGM (iPro™2 Professional CGM, Medtronic, USA) data in 61 patients each with FCPD and T2D who were matched for glycated hemoglobin A1c (HbA1c) and duration of diabetes. GlyCulator2 software was used to estimate the CGM-derived measures of GV (SD, mean amplitude of glycemic excursion [MAGE], continuous overall net glycemic action [CONGA], absolute means of daily differences [MODD], M value, and coefficient of variance [%CV]), hypoglycemia (time spent below 70 mg/dL, AUC below 70 mg/dL, glycemic risk assessment diabetes equation hypoglycemia, Low Blood Glucose Index), and hyperglycemia (time spent above 180 mg/dL at night [TSA > 180], AUC above 180 mg/dL [AUC > 180], glycemic risk assessment diabetes equation hyperglycemia, High Blood Glucose Index [HBGI], and J index). The correlation of GV indices with HbA1c, duration of diabetes, and demographic and biochemical parameters were also assessed. All the CGM-derived measures of GV (SD, MAGE, CONGA, MODD, and %CV), except M value, were significantly higher in the FCPD group than in the T2D group (P < 0.05). Measures of hyperglycemia (TSA >180, AUC >180, HBGI, and J index) were significantly higher in the FCPD group than in the T2D group (P < 0.05). The measures of hypoglycemia were not significantly different between the two groups. All the hyperglycemia indices showed a positive correlation with HbA1c in both groups. FCPD is associated with higher GV than is T2D. The findings of higher postprandial glycemic excursions in patients with FCPD could have potential therapeutic implications.

Coping strategies in people with type 1 diabetes on insulin pump treatment.

In stressed people with diabetes (PWD) glycemia varies idiosyncratically. Coping is a relatively stable personality trait that assesses how individual deals with stress. Insulin pumps are considered the best insulin delivery system but require more attention from PWD, virtually increasing stress levels. Aim of our study was to evaluate the coping strategies adopted by a pool of participants with type 1 diabetes mellitus (T1DM) on continuous subcutaneous insulin infusion (CSII) therapy. We collected data from 158 people with T1DM on CSII. COPE-NVI Test was administered and we also collected: hemoglobin A1c (HbA1c), 30 days of self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) records (the most recent). From SMBG and CGM records we computed Kovatchev indexes. We investigated bivariate correlations between HbA1c, indexes and the results of the COPE-NVI Test; the analysis was performed on the total sample and on two sub-groups: men and women. The participants were then divided into clusters and non-parametric tests were performed. Regarding bivariate correlation, at a level of significance of 95%, several statistically significant correlations were found in the total sample and in both men and women sub-groups. Regarding non-parametric tests, we observed statistically significant results in clusters of the total sample and clusters of the sub-group formed by men, divided according to Low Blood Glucose Index (LBGI) risk, showing a different use of certain coping strategies. Assessing coping strategies preferably used by PWD is useful to select PWD in need of counseling and empowerment-oriented training in order to improve glycemic outcomes and decrease severe hypo/hyperglycemic events.

Defining High Glycemic Variability in Type 1 Diabetes: Comparison of Multiple Indexes to Identify Patients at Risk of Hypoglycemia

Background: International consensus on the use of continuous glucose monitoring (CGM) recommends coefficient of variation (CV) as the metric of choice to express glycemic variability (GV) with a cutoff of 36% to define unstable diabetes. Even though, CV is associated with hypoglycemia in type 2 diabetes patients, the evidence on the use of one particular measure of GV in type 1 diabetes (T1DM) patients as a predictor of hypoglycemia is limited. Methods: A cohort of T1DM ambulatory patients was evaluated using CGM. Number and incidence rate of events <54 and <70 mg/dL were calculated. Bivariate and multivariate analysis of different glycemic indexes and clinical variables were performed to identify those associated with hypoglycemia. Receiver operating characteristic (ROC) curve analysis for each of the glycemic indexes was performed to define the best index and its optimal cutoff threshold to discriminate patients with events of hypoglycemia. Results: Seventy-three patients were included. A total of 128 events <54 mg/dL were recorded in 34 patients, and 350 events <70 mg/dL were registered in 51 patients. CV was the only variable significantly associated with hypoglycemia <54 mg/dL in the multivariate analysis (adjusted relative risk [aRR] 1.44, 95% confidence interval [CI]: 1.10-1.88, P = 0.008). CV, HbA1c (glycated hemoglobin), and mean glucose were associated with events <70 mg/dL. ROC curve analysis showed that, among GV metrics, CV had the best performance to discriminate patients with events <54 mg/dL (area under the curve [AUC] 0.87, 95% CI: 0.79-0.95) and events <70 mg/dL (AUC 0.79, 95% CI: 0.68-0.90) with optimal cutoff thresholds values of 34% and 31%, respectively. Among glycemic risk (GR) indexes, low blood glucose index (LBGI) showed the best performance. Conclusions: This analysis shows that CV is the best GV index, and LBGI the best GR index, to identify patients at risk of clinically significant hypoglycemia and hypoglycemia alert events in T1DM patients.

Addition of canagliflozin to insulin improves glycaemic control and reduces insulin dose in patients with type 2 diabetes mellitus: A randomized controlled trial.

The aim of this study was to evaluate the efficacy of canagliflozin in reducing the required insulin dose and the risk of hypoglycaemia in type 2 diabetes (T2D). This study was conducted in patients with T2D treated with insulin. They were randomly assigned to the control (n = 17) and canagliflozin (n = 17, plus 100 mg/day canagliflozin) groups. In both groups, a defined insulin dose adjustment protocol was applied to achieve the same level of glycaemic control. The change from baseline in daily insulin dose was significantly smaller in the canagliflozin group (3.9 units/day) than in the control group (13.4 units/day; P = 0.040). Low blood glucose index and predicted % of blood glucose (BG) <70 mg/dL, which are hypoglycaemia-related variables, worsened significantly in the control group but both remained unchanged in the canagliflozin group. The standard deviation for night-time BG levels improved significantly only in the canagliflozin group. Supplementation of insulin therapy with 100 mg canagliflozin in patients with T2D reduced the required insulin dose and hypoglycaemic risk and flattened night-time glycaemic fluctuations while maintaining the same level of glycaemic control.

917-P: Prediction Abilities for a Risk of Hypoglycemia in Glycemic Variability Metrics

We investigated prediction abilities for a risk of hypoglycemia (R-hypo) in intermediate-term glycemic variability metrics (GVM) using continuous glucose monitoring (CGM) data. We cross-sectionally investigated CGM (FreeStyle Libre Pro) data of 104 patients with type 2 diabetes whose 24 h glucose levels were measured continuously for 13 days during hospitalization. Values for 13 days in all GVM were evaluated. We have proposed GVM as follows: mean of daily difference 1 (MODD1) ÷ mean glucose level (MGL) × 100 (MODD1/M), mean absolute glucose (MAG) ÷ MGL × 100 (MAG/M), and glycemic variability percentage (GVP) ÷ MGL × 100 (GVP/M). The standard deviation (SD), MODD1, MAG, GVP, and MGL were significantly negatively correlated to the low blood glucose index (LBGI) (r=-0.34∼-0.68, p&amp;lt;0.001). The coefficient of variation (CV), MODD1/M, MAG/M and GVP/M were significantly positively correlated to the LBGI (r=+0.22∼+0.53, p=0.03∼&amp;lt;0.001). The prediction ability for LBGI&amp;gt;2.5 in MAG/M was significantly higher than that in CV and GVP/M. The prediction ability for LBGI&amp;gt;5.0 in MAG/M was significantly higher than that in CV, MODD1/M and GVP/M. The prediction ability for LBGI&amp;gt;2.5 was higher than that for LBGI&amp;gt;5.0 in MODD1/M (Table). The GVM which are divided by MGL should be used to predict a R-hypo. Among them, MAG/M has the highest prediction ability for a R-hypo. In addition, MODD1/M reflects a midium R-hypo more strongly than a high R-hypo. Disclosure S. Takeishi: None. H. Tsuboi: None.

279-OR: How to Use rtCGM Data to Predict Future Severe Hypoglycemia?

We combined rtCGM-baseline-data of the HypoDE and DIAMOND study to predict future severe hypoglycemia (SH; third party assistance for recovery). At baseline, in both studies, the rtCGM system (Dexcom G4) was masked. We analyzed the data of 127 study participants in the control groups with type 1 diabetes (age 46.0 ±11.5 years; diabetes duration 22.4 ±14.1 years, A1c 7.9 ±1.0%) using SMBG during the trials. According to the consensus statement we used the following hypoglycemia-specific predictors: % of glucose values &amp;lt; 70mg/dl and &amp;lt;54 mg/dl, the number of such events, Low Blood Glucose Index (LBGI), and the coefficient of variation (CV). After baseline, a total of 44 severe hypoglycemic events were observed during the following 6-months; with 12.6% affected participants. We analyzed the area under the Receiver Operating Characteristics (ROC) curves and determined cut-off values with an optimal sensitivity and specificity (Table). All hypoglycemia-related parameters, except CV, were able to predict SH during the next 6 months significantly better than prediction by chance; e.g., if ≥ 7% of glucose values were &amp;lt; 70 mg/dl, the probability for SH in the next 6-months was 23.4%. CGM-derived hypoglycemic outcome parameters might help to identify people with diabetes who have an increased risk for future hypoglycemia. These results should be corroborated by non-masked use of rtCGM but might also be useful in patients using SMBG. Disclosure N. Hermanns: Advisory Panel; Self; Abbott. Board Member; Self; Eli Lilly and Company. Research Support; Self; Abbott, Berlin-Chemie AG, Dexcom, Inc., Roche Diabetes Care. Speaker's Bureau; Self; Berlin-Chemie AG, Dexcom, Inc., Novo Nordisk A/S. D. Ehrmann: Advisory Panel; Self; Medtronic. Speaker's Bureau; Self; Abbott, Berlin-Chemie AG, Dexcom, Inc. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. G. Freckmann: Advisory Panel; Self; Abbott, Novo Nordisk Pharma GmbH, Sensile Medical AG. Consultant; Self; Metronom Health Europe, Roche Diabetes Care, Sanofi. Speaker's Bureau; Self; Ascensia Diabetes Care, Dexcom, Inc., Ypsomed AG. L. Heinemann: Consultant; Self; Roche Diabetes Care. Research Support; Self; Dexcom, Inc. Stock/Shareholder; Self; Profil Institute for Metabolic Research, ProSciento. Funding Dexcom, Inc.

1230-P: Addition of Canagliflozin to Insulin Improves Glycemic Control and Reduces Insulin Dose in Patients with Type 2 Diabetes Mellitus: A Study Based on Continuous Glucose Monitoring

Background and Aims: No studies have compared insulin dose or hypoglycemic risks between type 2 diabetes mellitus (T2DM) treated with and without Sodium glucose cotransporter-2 (SGLT2) inhibitors using defined criteria for insulin dose adjustment to achieve the same level of glycemic control. The aim of this study was to evaluate the efficacy of canagliflozin in reducing the required insulin dose and the risk of hypoglycemia in T2DM. Materials and Methods: This randomized, two-group comparative study was conducted in 34 patients treated with insulin alone or combined with oral hypoglycemic agents. They were randomly assigned to the control (n=17, insulin) and canagliflozin (n=17, insulin plus 100 mg/day) groups and wore a continuous glucose monitoring (CGM, ipro2®) for 14 days. In both groups, a defined insulin dose adjustment protocol was applied to achieve the same level of glycemic control. Results: The change from baseline in daily insulin dose was significantly smaller in the canagliflozin group (3.9 units/day) than control group (13.4 units/day; p = 0.040). Low blood glucose index (LBGI) and predicted % of blood glucose (BG) &amp;lt;70 mg/dL, which are hypoglycemia-related parameters, worsened significantly in the control group, but both remained unchanged in the canagliflozin group. The SD for nighttime BG levels improved significantly only in the canagliflozin group. Participants’ body weight (BW) decreased in both groups, but the change in BW showing a significant decrease in the canagliflozin group. Conclusion: Supplementation of insulin therapy with 100 mg canagliflozin in patients with T2DM reduced the required insulin dose and hypoglycemic risk and flattened nighttime glycemic fluctuations while maintaining the same level of glycemic control. The addition of SGLT2 inhibitors may provide several benefits to patients on insulin therapy, enabling them to maintain strict glycemic control. Disclosure K. Torimoto: None. Y. Okada: None. Y. Sato: None. Y. Tanaka: None.

948-P: Analysis of Key Metrics for Continuous Glucose Monitoring Data in Adults without Diabetes

Continuous glucose monitoring (CGM) may help to improve diabetes therapy. An international consensus paper calls for standards in the CGM data analysis, suggesting reports of standardized “key metrics.” Data from people without diabetes might be useful in the interpretation of the suggested key metrics. In this study, 39 adults without diabetes used two iscCGM systems (FreeStyle Libre) in parallel for the 14-day sensor life. Based on collected iscCGM data the key metrics mean glucose, standard deviation, glycemic variability (CV), time in various glucose ranges, low blood glucose index (LBGI) and high blood glucose index (HBGI) were calculated. Instead of estimated A1C, its recently proposed replacement, the glucose management index (GMI), was calculated. Out of 78 sensors, 73 sensors achieved a data capture rate of at least 80%, the minimum required by the consensus statement. Direct comparison of the two sensors worn in parallel was possible for 35 subjects. Specific results are provided in the Table. In this study, some key metrics calculated from iscCGM data of adults without diabetes showed considerable variation between subjects as well as within subjects. Although the reliability of these key metrics depends on how accurately CGM data depict “true” glucose levels, these findings may be helpful in the interpretation of these key metrics in diabetes patients. Disclosure G. Freckmann: Advisory Panel; Self; Abbott, Novo Nordisk Pharma GmbH, Sensile Medical AG. Consultant; Self; Metronom Health Europe, Roche Diabetes Care, Sanofi. Speaker's Bureau; Self; Ascensia Diabetes Care, Dexcom, Inc., Ypsomed AG. S. Pleus: None. S. Ulbrich: None. D. Waldenmaier: None. A. Baumstark: None. M. Link: None. C. Haug: None.

A personalized diet and exercise recommender system for type 1 diabetes self-management: An in silico study

Management of diet and exercise levels needs to be personalized for patients with Type 1 Diabetes (T1D) to reduce the number of hypoglycemia events and to achieve a good glycemic control. This study developed a model-based Recommender system that could provide an (optimal) personalized intervention on diet and exercise for T1D patients, which could be potentially implemented as a mobile application (app) for self-management of T1D in the future work. At each intervention time, the Recommender makes prediction of blood glucose based on a patient-specific model of glucose dynamics, and then provides optimal interventions, which could be a meal/snack size or a target heart rate during exercise, by minimizing a risk function with constraints under a future time horizon. Simulations were conducted to evaluate the Recommender through 30 virtual subjects generated from a modified UVa/Padova simulator with an added exercise-glucose subsystem. The performance of the Recommender was compared to two self-management schemes: the Starter scheme and the Skilled scheme, where the Skilled represents an off-line optimal scheme providing a lower bound on the risk index. Compared to the Starter, the Recommender reduced the mean Low Blood Glucose Index by 84% and reduced the Blood Glucose Risk Index by 49% (P < 0.05), and it had comparable performance as the Skilled. The Recommender also reduced the number of hypoglycemia events during and post-exercise compared to the Starter and the Skilled.

Hybrid Closed-Loop Control Is Safe and Effective for People with Type 1 Diabetes Who Are at Moderate to High Risk for Hypoglycemia.

Background: Typically, closed-loop control (CLC) studies excluded patients with significant hypoglycemia. We evaluated the effectiveness of hybrid CLC (HCLC) versus sensor-augmented pump (SAP) in reducing hypoglycemia in this high-risk population. Methods: Forty-four subjects with type 1 diabetes, 25 women, 37 ± 2 years old, HbA1c 7.4% ± 0.2% (57 ± 1.5 mmol/mol), diabetes duration 19 ± 2 years, on insulin pump, were enrolled at the University of Virginia (N = 33) and Stanford University (N = 11). Eligibility: increased risk of hypoglycemia confirmed by 1 week of blinded continuous glucose monitor (CGM); randomized to 4 weeks of home use of either HCLC or SAP. Primary/secondary outcomes: risk for hypoglycemia measured by the low blood glucose index (LBGI)/CGM-based time in ranges. Results: Values reported: mean ± standard deviation. From baseline to the final week of study: LBGI decreased more on HCLC (2.51 ± 1.17 to 1.28 ± 0.5) than on SAP (2.1 ± 1.05 to 1.79 ± 0.98), P < 0.001; percent time below 70 mg/dL (3.9 mmol/L) decreased on HCLC (7.2% ± 5.3% to 2.0% ± 1.4%) but not on SAP (5.8% ± 4.7% to 4.8% ± 4.5%), P = 0.001; percent time within the target range 70-180 mg/dL (3.9-10 mmol/L) increased on HCLC (67.8% ± 13.5% to 78.2% ± 10%) but decreased on SAP (65.6% ± 12.9% to 59.6% ± 16.5%), P < 0.001; percent time above 180 mg/dL (10 mmol/L) decreased on HCLC (25.1% ± 15.3% to 19.8% ± 10.1%) but increased on SAP (28.6% ± 14.6% to 35.6% ± 17.6%), P = 0.009. Mean glucose did not change significantly on HCLC (144.9 ± 27.9 to 143.8 ± 14.4 mg/dL [8.1 ± 1.6 to 8.0 ± 0.8 mmol/L]) or SAP (152.5 ± 24.3 to 162.4 ± 28.2 [8.5 ± 1.4 to 9.0 ± 1.6]), P = ns. Conclusions: Compared with SAP therapy, HCLC reduced the risk and frequency of hypoglycemia, while improving time in target range and reducing hyperglycemia in people at moderate to high risk of hypoglycemia.

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.

Continuous glucose monitoring in pregnant women with type 1 diabetes: an observational cohort study of 186 pregnancies

Aims/hypothesisThe aim of this study was to analyse patterns of continuous glucose monitoring (CGM) data for associations with large for gestational age (LGA) infants and an adverse neonatal composite outcome (NCO) in pregnancies in women with type 1 diabetes.MethodsThis was an observational cohort study of 186 pregnant women with type 1 diabetes in Sweden. The interstitial glucose readings from 92 real-time (rt) CGM and 94 intermittently viewed (i) CGM devices were used to calculate mean glucose, SD, CV%, time spent in target range (3.5–7.8 mmol/l), mean amplitude of glucose excursions and also high and low blood glucose indices (HBGI and LBGI, respectively). Electronic records provided information on maternal demographics and neonatal outcomes. Associations between CGM indices and neonatal outcomes were analysed by stepwise logistic regression analysis adjusted for confounders.ResultsThe number of infants born LGA was similar in rtCGM and iCGM users (52% vs 53%). In the combined group, elevated mean glucose levels in the second and the third trimester were significantly associated with LGA (OR 1.53, 95% CI 1.12, 2.08, and OR 1.57, 95% CI 1.12, 2.19, respectively). Furthermore, a high percentage of time in target in the second and the third trimester was associated with lower risk of LGA (OR 0.96, 95% CI 0.94, 0.99 and OR 0.97, 95% CI 0.95, 1.00, respectively). The same associations were found for mean glucose and for time in target and the risk of NCO in all trimesters. SD was significantly associated with LGA in the second trimester and with NCO in the third trimester. Glucose patterns did not differ between rtCGM and iCGM users except that rtCGM users had lower LBGI and spent less time below target.Conclusions/interpretationHigher mean glucose levels, higher SD and less time in target range were associated with increased risk of LGA and NCO. Despite the use of CGM throughout pregnancy, the day-to-day glucose control was not optimal and the incidence of LGA remained high.