Continuous Glucose Monitoring Sensor Research Articles

Chronotype, chrononutrition and glucose tolerance among prediabetic individuals: research protocol for a prospective longitudinal study Chrono-DM™

BackgroundChronotype and chrononutrition, both are emerging research interests in nutritional epidemiology. However, its association with glycemic control in the Asia population is less clear. A better understanding of how activity/eating time can influence glucose levels in Asian prediabetic individuals may improve strategies for blood glucose control in Asian countries. The present paper describes the research protocol which aims to determine the associations of chronotype and chrononutrition with glucose tolerance among Malaysian prediabetic individuals.MethodsThis is a prospective longitudinal study named Chrono-DM™, that targets to recruit 166 newly diagnosed prediabetic individuals from the community clinics in Malacca, Malaysia. Respondents will be followed-up for 6 months: (1) baseline (1st oral glucose tolerance test (OGTT)); (2) second visit (at 3rd month); and (3) third visit (2nd OGTT at 6th month). Data collection includes sociodemographic and anthropometry measurements (weight, height, body fat, visceral fat, waist and hip circumference). Dietary intake and meal timing are collected using the 3-day dietary record while data on sleep pattern, light exposure, chronotype and chrononutrition will be collected using validated questionnaires. Physical activity will be recorded using a validated IPAQ questionnaire and pedometer during periods of using continuous glucose monitoring (CGM) sensor. CGM, fasting blood sugar (FBS), OGTT and HbA1c are performed to assess glycemic outcomes.DiscussionThe Chrono-DM™ study represents a novel approach to determining the association of chronotype and chrononutrition with glycemic control. We anticipate that this study will not only review the association of chronotype with glycemia measure but also provide greater insight into optimal meal time for glycemic control among prediabetic individuals in the Asian population.Trial registrationNCT05163964 (Clinicaltrial.gov). Trial registration date: 20 December 2021.

Comparison of the stability of Mucor-derived flavin adenine dinucleotide-dependent glucose dehydrogenase and glucose oxidase

Long-term stability at near-body temperature is important for continuous glucose monitoring (CGM) sensors. However, the stability of enzymes used in CGM sensors has often been evaluated by measuring their melting temperature (Tm) values and by short heat treatment but not at around 37°C. Glucose oxidase (GOD) is used in current CGM sensors. In this study, we evaluated the stability of modified Mucor-derived flavin adenine dinucleotide-dependent glucose dehydrogenase (designated Mr144-297) with improved thermal stability at medium to high temperatures and compared it with that of GOD. The Tm value of Mr144-297 was 61.6±0.3°C and was similar to that of GOD (61.4±1.2°C). However, Mr144-297 was clearly more stable than GOD at 40°C and 55°C. At 37°C, the stability of a carbon electrode with immobilized Mr144-297 was higher than that of an electrode with GOD. Our data indicate that Mr144-297 is a more suitable enzyme for CGM sensors than is GOD.

Development of the Extended Infusion Set and Its Mechanism of Action.

Continuous subcutaneous insulin infusion (CSII, or insulin pump) and continuous glucose monitoring (CGM) sensors have been increasingly used and associated with improved glycemic control by people with type 1 diabetes and insulin-requiring type 2 diabetes. Commonly used infusion sets in most CSII systems are limited to a wear time of 3 days. In contradistinction, CGM sensors are currently approved for seven and more days of wear. With the motivation to provide a 7-day infusion set that matches the CGM wear time and to improve patient experience, the recently CE-marked and FDA 510k-cleared Medtronic extended infusion set (EIS) was designed.s The EIS offers enhanced new features that include use for up to 7 days, improved convenience, comfort, and better quality of life for insulin pump users.

Continuous Glucose Profiles in Healthy People With Fixed Meal Times and Under Everyday Life Conditions.

The increased use of continuous glucose monitoring (CGM) and automated insulin delivery systems raises the question about therapeutic targets for glucose profiles in people with diabetes. This study aimed to assess averaged pre- and postprandial glucose profiles in people without diabetes to provide guidance for normal glucose patterns in clinical practice. For that, number and timing of meal intake were predefined. To assess glucose traces in 36 participants without diabetes (mean age = 23.7 ± 5.7 years), CGM was performed for up to 14 days, starting with a run-in phase (first 3 days, excluded from analysis) followed by 4 days with fixed meal times at 8:00 am, 1:00 pm, and 6:00 pm and the remaining 7 days spent under everyday life conditions. Data from two simultaneously worn CGM sensors were averaged and adjusted to capillary plasma-equivalent glucose values. Glucose data were evaluated through descriptive statistics. Median glucose concentration on days with fixed meal times and under everyday life conditions was 95.0 mg/dL (91.6-99.1 mg/dL, interquartile range) and 98.1 mg/dL (93.7-100.8 mg/dL), respectively. On days with fixed meal times, mean premeal glucose was 92.8 ± 9.4 mg/dL, and mean peak postmeal glucose was 143.3 ± 23.5 mg/dL. By defining the time of meal intake, a clear pattern of distinct postprandial glucose excursions in participants without diabetes could be demonstrated and analyzed. The presented glucose profiles might be helpful as an estimate for adequate clinical targets in people with diabetes.

Viscoelastic and Viscoplastic Glucose Theory Applications (VGT #45): Applying the Physics Concepts of Viscoelasticity and Viscoplasticity to Estimate the “Difference of Relative Energy Level” Measured by the Hysteresis Loop Area, a Biophysical and Engineering Phenomenon, along with the Carbs/Sugar Intake Grams, a Biomedical-Related Key Lifestyle Detail, from Inputs of Three Distinctive PPG Ranges to Provide a Guesstimated Damage Level of

Recently, the author applied theories of viscoelasticity and viscoplasticity from engineering and physics along with wave theory and energy theory from physics to conduct his biomedical research on output biomarkers of postprandial plasma glucose (PPG), which is one of the key indicators for diabetes, resulting from the input biomarker of carbs/sugar intake amounts, which is a major cause for hyperglycemia. In this article, he extracted three sub-groups of PPG waveforms and datasets during a long period of ~4 years from 5/8/2018 to 3/16/2022. All of the PPG data are collected via a continuous glucose monitoring (CGM) sensor device at 15-minute time intervals (96 data each day) over 1,408 days and 4,310 meals that contain 56,030 PPG data. These three PPG sub-groups are listed below: Low PPG - 2,885 meals with 6.6 carbs/sugar grams and 122 mg/dL average PPG; Medium PPG - 1,298 meals with 23.9 carbs/sugar grams and 132 mg/dL average PPG; High PPG - 127 meals with 73.7 carbs/sugar grams and 153 mg/dL average PPG. There are three specific calculation steps for the numerical operation.

Linear Elastic Glucose Theory (LEGT #40) & Viscoelastic or Viscoplastic Glucose Theory (VGT #76): Comparing the Results from using LEGT Prediction Tool Versus VGT Energy Tool to Study Postprandial Plasma Glucose (Symptom) Versus Fasting Plasma Glucose, Carbohydrates & Sugar Intake Amounts, and Post-Meal Walking Steps (causes) Based on GH-method: Math-Physical Medicine (No. 666)

The author applied linear elasticity theory (LEGT) to conduct various medical research work, resulting in 39 papers starting with paper No. 346 beginning on 10/14/2020. In addition, he utilized viscoelastic and viscoplastic theories (VGT) to conduct 75 sets of studies regarding symptoms (strains ε) versus multiple causes (stresses σ) in the biomedical field with paper No. 578 beginning on 1/8/2022. In this article, he used 5,864 data of postprandial plasma glucose (PPG), fasting plasma glucose (FPG), carbohydrates and sugar intake amounts (carbs), and post-meal walking steps (steps) collected from a continuous glucose monitoring (CGM) sensor device over 4 years from 5/8/2018 to 5/13/2022.

Viscoelastic or Viscoplastic Glucose Theory (VGT #78): A Sensitivity Analysis of the Baseline Selection for Normalized Fasting Plasma Glucose to Study Two Symptoms of Postprandial Plasma Glucose and Glucose Fluctuation Versus 3 Causes of FPG, Carbohydrates/Sugar Grams, and Post-Meal Walking Steps Based on GH-Method: Math-Physical Medicine and the Utilization of the VGT Energy Tool (No. 668)

The author has applied viscoelastic and viscoplastic theories (VGT) to conduct 77 different sets of studies regarding symptoms (strains ε) versus multiple causes (stresses σ) in the biomedical field beginning with paper No. 578 on 1/8/2022. In this article, he used 7,330 data of postprandial plasma glucose (PPG), glucose fluctuation (GF) along with their related data, such as fasting plasma glucose (FPG), carbohydrates/sugar intake amounts (carbs), and post-meal walking steps (steps). The collected data is from a continuous glucose monitoring (CGM) sensor device over 4 years from 5/8/2018 to 5/13/2022. He utilizes the following defined VGT equation from engineering and physics to address the “time-dependency characteristic” of biomedical symptoms and their causes to establish several stress-strain diagrams (i.e. cause-symptom diagrams) in a space domain (SD) via various hysteresis loops: strain = ε (PPG or GF) = individual GF value at the present time Stress = σ (based on the change rate of strain, PPG rate, or GF rate, multiplying with a chosen viscosity factor η, FPG, carbs, or steps) = η * (dε/dt) = η * (d-strain/d-time) = (viscosity factor η using individual FPG, carbs, or steps at present time) * (PPG or GF at present time - PPG or GF at a previous time) He defines GF as the maximum PPG minus the minimum PPG value. The 3 causes or 3 viscosity factors are further grouped into two different categories, where the first category is not used in this paper:

Viscoelastic or Viscoplastic Glucose Theory (VGT #75): A Study of 4 Postprandial Plasma Glucose Fluctuation (PPG-GF) Components Versus Carbohydrates & Sugar Intake Amounts (Carbs) and Post-Meal Walking Steps (Steps) using the Collected Data over the Past 4 Years from 5/8/2018 to 5/10/2022 Based on VGT Energy Tool and GH-Method: Math-Physical Medicine (No. 665)

Various medical research work has proven that the glucose fluctuation (GF) connects various diabetes complications, such as cardiovascular disease (CVD), coronary heart disease (CHD), chronic kidney disease (CKD), diabetic retinopathy (DR), stroke, liver cirrhosis, Alzheimer’s disease, and parkinsonism. The author defines GF as maximum glucose minus minimum glucose In this article, he uses his collected continuous glucose monitoring (CGM) sensor postprandial plasma glucose (PPG) data over 4 years from 5/8/2018 to 5/10/2022 as the base. But, he extends his GF definition into a wider range which includes the following 4 components:

Viscoelastic or Viscoplastic Glucose Theory (VGT #79): A Sensitivity Analysis on Selecting Normalization Baselines for 3 Viscosity Factors, Fasting Plasma Glucose, Sleep Scores, and Sleep Hours to Study a Single Symptom of HbA1C Versus 3 Measured Causes using 3 Domain Analyses of Time, Space, and Frequency Based on GH-Method: Math-Physical Medicine (No. 669)

The author applied viscoelastic and viscoplastic theories (VGT) to conduct 78 sets of studies regarding single or multiple symptoms (strains ε) versus multiple causes (stresses σ) in the biomedical field with paper No. 578 beginning on 1/8/2022. In this article, he used 5,864 data of daily HbA1C (A1C), daily fasting plasma glucose (FPG), daily sleep scores, and daily seeping hours collected from a continuous glucose monitoring (CGM) sensor device and other health measuring devices, such as a Fitbit watch, Oura ring, and eclaireMD chronic software over ~7.5 years from 1/1/2015 to 5/15/2022. The reason FPG was chosen first as the major cause of A1C is that it reflects the health status of pancreatic beta cells that produce and release insulin to control glucose levels. Although PPG numerically contributes the bulk of daily average glucose, PPG’s biggest contribution is also from the quality and quantity of insulin. His daily sleep score is based on the following 16 different types of input items: • sleeping hours • wakeup times during sleep • annoying issues • degree of freshness & restfulness • degree of wake-up headache • degree of night dreams • comfortable room temperature • degree of physical sickness • sleep pattern disturbance • itchy skin • numbness of hands or feet • hungry feeling during sleep • feeling sleepy at wakeup • leg muscle cramp • cold feeling of legs or feet • snoring during sleep

Viscoelastic or Viscoplastic Glucose Theory (VGT #77): A Comparison Study of Applying the VGT Energy Tool to Study the Symptom of Glucose Fluctuation (GF) of Postprandial Plasma Glucose Versus 3 Causes of Fasting Plasma Glucose, Carbohydrates & Sugar Intake Amounts, Post-Meal Walking Steps from using Two Different Input Datasets of Both Measured Causes and Normalized Causes Based on GH-Method: Math-Physical

The author has applied viscoelastic and viscoplastic theories to conduct 76 different sets of studies regarding symptoms (strains ε) versus multiple causes (stresses σ) in the biomedical field beginning with paper No. 578 on 1/8/2022. In this article, he used 7,330 data of glucose fluctuation (GF) of postprandial plasma glucose (PPG), fasting plasma glucose (FPG), carbohydrates and sugar intake amount (carbs), and post-meal walking steps (steps) collected from a continuous glucose monitoring (CGM) sensor device over 4 years from 5/8/2018 to 5/13/2022. He utilizes the following defined VGT equation from engineering and physics to address the “time-dependency characteristic” of biomedical symptoms and causes to establish several stress-strain diagrams (i.e. cause-symptom diagrams) in a space domain (SD): strain = ε (GF) = individual GF value at the present time Stress = σ (based on the change rate of strain, GF rate, multiplying with a chosen viscosity factor η, FPG, carbs, or steps) = η * (dε/dt) = η * (d-strain/d-time) = (viscosity factor η using individual FPG, carbs, or steps at present time) * (GF at present time - GF at a previous time) He defines GF as the maximum PPG minus the minimum PPG value. Where the 3 causes or 3 viscosity factors are grouped into two different categories for this study: First category: Non-modified original data from various measurements FPG = an average glucose value over 7 hours of sleep time Carbs = estimated carbs/sugar intake grams for each meal

Data-Driven Supervised Compression Artifacts Detection on Continuous Glucose Sensors.

Continuous Glucose Monitoring (CGM) sensors micro-invasively provide frequent glucose readings, improving the management of Type 1 diabetic patients' life and making available reach data-sets for retrospective analysis. Unlikely, CGM sensors are subject to failures, such as compression artifacts, that might impact on both real-time and respective CGM use. In this work is focused on retrospective detection of compression artifacts. An in-silico dataset is generated using the T1D UVa/Padova simulator and compression artifacts are subsequently added in known position, thus creating a dataset with perfectly accurate faulty/not-faulty labels. The problem of compression artifact detection is then faced with supervised data-driven techniques, in particular using Random Forest algorithm. The detection performance guaranteed by the method on in-silico data is satisfactory, opening the way for further analysis on real-data.

A Power-Harvesting CGM Chiplet Featuring Silicon-Based Enzymatic Glucose Sensor.

Diabetes has become a leading cause of death and disability in the past decades. Continuous glucose monitoring (CGM) is a prevailing technique to determine the glucose level and provide in-time treatment. However, conventional CGM systems combine an electrochemical sensor with a CMOS chip, suffering from bulky size and interface issues. Integrating the CGM sensor on silicon is potential to miniaturize the CGM system and reduce the cost, while the recent silicon-based sensors show limited detection range and sensitivity. In this work, we present a silicon-based CGM chip let with wireless power transfer (WPT) and real-time wireless telemetry. Fabricated on a single silicon substrate, the chiplet consists of a silicon-based CGM sensor, a power-harvesting wireless-telemetry chip, and a silicon-based antenna. Measured results show that the chip let achieves a sensitivity of 4 μA.mM.cm-2 and a linear detection range of 0-10 mM. Based on WPT and backscattering communication, the chip let consumes 18.8 μ W power in glucose telemetry.

A Nonlinear State Observer for the Bi-Hormonal Intraperitoneal Artificial Pancreas.

Currently, continuous glucose monitoring sensors are used in the artificial pancreas to monitor blood glucose levels. However, insulin and glucagon concentrations in different parts of the body cannot be measured in real-time, and determining body glucagon sensitivity is not feasible. Estimating these states provides more information about the current system status, facilitating improved decision-making by the model-based controller. In this regard, the aim of this paper is to design a nonlinear high-gain observer for a bi-hormonal artificial pancreas in the presence of measurement noises, model uncertainties, and disturbances. The model used in the observer is based on an existing intraperitoneal nonlinear animal model in the literature. This model is modified by assuming that insulin can directly transfer from the peritoneal cavity to the bloodstream. Based on a set of realistic assumptions, one model is considered after each hormone infusion, and two observers are separately designed. The model is divided into the insulin-phase and glucagon-phase models based on a set of realistic assumptions. Thereafter, two high-gain observers are designed separately for these phases contributing to estimating the non-measurable states. The observer error is proven to be locally uniformly ultimately bounded, and it is verified that any asymptotically stable control laws remain stable in the presence of the observer. The performance of the observers with different gains is evaluated for a scenario with multiple insulin and glucagon infusions. The proposed observer converges to a finite error, according to the results. Clinical relevance- In Type 1 diabetic patients, the developed observer can be employed in a closed-loop artificial pan-creas to improve the performance of model-based controllers. It estimates the key states, which are necessary for forecasting the body's response to insulin and glucagon boluses.

Glucose Prediction using Wide-Deep LSTM Network for Accurate Insulin Dosing in Artificial Pancreas.

Closed-loop diabetes management has been shown to indicate improved glycaemic control and better compliance over open loop diabetes management. Currently, commercially available diabetes management devices rely on continuous glucose monitoring (CGM) sensors which monitor glucose levels from the interstitial fluid (ISF). As there exists a physiological delay between the blood glucose levels compared to the ISF glucose levels, it is crucial to predict or forecast glucose levels, in order to prevent hyperglycaemic events due to delayed insulin dosing. Accuracy of the forecast also needs to be optimum such that overdosing on insulin does not lead to hypoglycaemia. In this paper, we describe a novel Long Short Term Memory (LSTM) network which follows a wide and deep approach for different features to deliver an accurate glucose prediction output. It achieved a Mean Absolute Relative Difference (MARD) of 2.61 and Root Mean Squared Error (MSE) of 5.04. Clinical relevance- This work is relevant for closed-loop diabetes management devices, which are currently being used to manage Type 1 Diabetes (T1D).

67-LB: Nursing Perspectives on Hospital Use of Dexcom Continuous Glucose Monitor in Patients with Suspected or Known COVID-19 during Insulin Infusion

The coronavirus disease 2019 pandemic resulted in the need for remote glucose monitoring in intensive care unit (ICU) patients requiring insulin infusions. While rarely reported, perceptions from nursing staff are critical for successful implementation and expanded use. A hybrid continuous glucose monitoring (CGM) and intermittent point of care (POC) glucose monitoring was developed at the study institution. After 1.5 years of protocol use, an anonymous survey was distributed electronically to ICU nurses to obtain their perspectives on CGM utilization. The survey employed a 1-5 Likert scale regarding CGM sensor insertion, accuracy, acceptability, and usability as well as perceptions on reduced room entry and reduced workload. Of the 51 surveys completed, 50 (98%) nurses reported they cared for an ICU patient enrolled in the CGM protocol and 31 (62%) started or replaced a CGM sensor. A representative sample of questions and responses is shown in table 1. ICU nurses overwhelmingly reported CGM was accurate, reduced their workload, provided safer patient care, and was preferred over POC glucose testing alone. These results confirm nursing acceptance and preference for CGM use within a hybrid glucose monitoring protocol in the ICU setting. Disclosure J. Pattison: None. K. M. Dungan: Advisory Panel; Dexcom, Inc., Consultant; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Elsevier, Nova Biomedical, Novo Nordisk, Tolerion, Inc., Other Relationship; UpToDate, Research Support; Abbott, Dexcom, Inc., Novo Nordisk, Sanofi, ViaCyte, Inc., Speaker’s Bureau; Academy for Continued Healthcare Learning, Cardiometabolic Health Congress, Medscape. E. Buschur: Advisory Panel; Beta Bionics, Inc., Research Support; Dexcom, Inc. M. C. Exline: Other Relationship; Abbott. L. G. Jones: None. C. May: None. M. Mcnett: None. K. Smetana: None. E. R. Faulds: Advisory Panel; Dexcom, Inc., Research Support; Dexcom, Inc., Speaker’s Bureau; Medscape.

885-P: Real-World Evidence on Health Care Costs Related to Self-Monitoring of Blood Glucose (SMBG) Compared with Continuous Glucose Monitoring (CGM) in Nonintensively Treated Type 2 Diabetes Mellitus (T2DM)

Background: Type 2 diabetes poses an economic burden to healthcare systems. For individuals with non-intensively treated T2DM, it is unclear whether the use of CGM would add health-economic value. The aim of this study was to compare the costs of SMBG compared to CGM in non-intensively treated T2DM. Methods: A retrospective cost-analysis using the IBM® MarketScan® Databases was conducted. T2DM patients aged ≥18 years who were using SMBG or initiated CGM between Jan 2018 and March 20were eligible for inclusion. Inclusion criteria included two consecutive claims for T2DM or one claim for T2DM and a claim for glucose lowering therapy, at least one pharmacy claim for SMBG strips or CGM sensors and continuous enrollment for 1-year before and after the index date. Individuals with CGM in pre-index period, pregnancy, rapid-acting insulin, glucagon, T1DM, gestational or secondary diabetes were excluded. SMBG and CGM patients were matched using a propensity score and all cause costs during a one-year follow-up period were compared. Results: A total of 3,498 patients were included in each matched cohort. For CGM patients, 77% used flash monitoring and 23% a real-time CGM. 35% of SMBG and 37% of CGM users were on basal insulin. Considering median values, SMBG total healthcare costs per person/year were $1,934 less vs. CGM users (p < 0.05) . SMBG patients had significantly lower pharmacy cost (-$2,257, p<0.05) and lower expenses for glucose-lowering treatments (-$1,045, p<0.05) than CGM. Both SMBG and CGM cohorts had similar costs related to inpatient and emergency room admissions. Conclusion: This analysis shows that SMBG is less costly than CGM in non-intensively treated T2DM patients and is associated with lower pharmacy costs, including glucose-lowering medications. Furthermore, no significant differences in the number of emergency room visits or hospitalizations are seen in SMBG and CGM users. Disclosure D. Kerr: Advisory Panel; Abbott Diabetes, Novo Nordisk A/S, Sanofi. Consultant; Evidation Health. Research Support; Novo Nordisk A/S. Stock/Shareholder; Glooko, Inc., Hi.Health. I. Duncan: Consultant; Onduo LLC. F. Giorgino: Advisory Panel; AstraZeneca, Boehringer Ingelheim International GmbH, Novo Nordisk. Consultant; Lilly Diabetes, Sanofi. Research Support; Lilly Diabetes, Roche Diabetes Care, Takeda Pharmaceutical Company Limited. E. Repetto: Employee; Roche Diabetes Care. C. Perkins: Employee; Roche Diagnostics USA. R. Maroun: Employee; Amgen Inc., Roche Diagnostics. M. Rivers: Employee; Roche Diabetes Care. A. Wu: None.

700-P: Use of Continuous Glucose Monitoring Does Not Alter Hypoglycemic Awareness in Patients with Type 2 Diabetes Mellitus

Background: There is limited evidence of continuous glucose monitoring (CGM) in patients with type 2 diabetes (T2D) . CGM use improves HbA1c and quality of life, however scant evidence exists on the effect of CGM use on hypoglycemia awareness in patients with T2D. Selecting patients with T2D at high risk and low risk for hypoglycemia, we hypothesized that CGM use would improve hypoglycemia awareness. Methods: We conducted a 16 week, single center, randomized study on patients [n=40, 50% females, mean (SD) age 59 years (12.5) , BMI 34.kg/m2 (8.1) ] with T2D [screening HbA1c 7.6% (1.2) ] who were deemed to be either high risk (n=22) or low risk (n=18) for hypoglycemia over 5 years using our previously established scoring system. Each participant wore 2 CGM sensors. Within each risk category, participants were randomized to wearing either 1 unblinded+1 blinded sensor (Freestyle Libre/Freestyle Libre Pro) or 2 blinded sensors (2 Freestyle Libre Pro) . Every 2 weeks was an in-person visit for CGM replacement. Clarke and Gold questionnaires were performed at the screening visit (week 0) , visit 4 (week 8) and visit 8 (week 16) . Participants in the unblinded CGM group were instructed to use the CGM as they wished. Participants in the blinded CGM group were instructed to continue with their usual finger stick program. Result: At baseline, there were no differences in the Gold or Clark questionnaires between participants at high or low risk for hypoglycemia over 5 years. At end-intervention, no differences were noted in the Gold or Clark questionnaires regardless of CGM assignment or baseline hypoglycemia risk category. The trajectory of Gold or Clark scores over the study were also not significantly different between groups regardless of CGM assignment or baseline hypoglycemia risk category. Conclusion: In patients with T2D, the use of an unblinded CGM did not affect the awareness of hypoglycemia, even in patients at high risk for hypoglycemia, as assessed by Gold and Clark scores. Disclosure A.Mehfooz: None. Y.Lee: None. Q.Wang: None. L.S.Chow: Other Relationship; Dexcom, Inc.

96-LB: Safety of Glucose Regulation by the Bionic Pancreas without Continuous Glucose Monitoring Input

The bionic pancreas (BP) continues to make all insulin dosing decisions autonomously when continuous glucose monitoring (CGM) data is not available. Basal insulin is given based on a profile determined autonomously by the BP when CGM data was available. Meals are announced as Breakfast, Lunch, or Dinner and by relative size (Usual, Less, More) ; insulin dosing is based on adaptation that occurred when CGM data was available. Insulin may be delivered, or temporarily suspended, autonomously by the BP in response to entered blood glucose (BG) values. At the end of the 13-week Pivotal RCT of the BP in type 1 diabetes, participants in the BP group could participate in a study testing the BP without CGM input. Unblinded CGM was replaced by blinded CGM not connected to the BP for ∼49.5 hours. Participants were to enter fingerstick BG values every ∼2 hours during the day and once overnight. Adherence with BG entry requirements by the 54 participants (aged 7-70) was high. There was no severe hypoglycemia or DKA. The frequency of CGM-measured hypoglycemia was similar during the BG Period to the RCT and Pre-Randomization periods. Hyperglycemia metrics were nominally higher during the BG Period than the RCT, but nominally lower than the Pre-randomization Baseline period. The BP can autonomously determine all insulin doses and achieve safe glucose control without CGM input when intermittent BG values are provided by the user. Disclosure C. A. Balliro: Consultant; Beta Bionics, Inc., Zealand Pharma A/S. Funding Funding from National Institute of Diabetes and Digestive and Kidney Diseases (#1UC4DK108612-01) . Funding and bionic pancreas devices from Beta Bionics, Inc. Fast-acting insulin aspart and insulin aspart provided by Novo Nordisk Insulin lispro by Eli LillyBlood glucose meters and test strips (Contour Next One Blood Glucose Monitoring System) provided by Ascensia Diabetes Care, Basel, CH. Continuous glucose monitor sensors and transmitters were purchased from Dexcom, Inc. at a discounted price.

98-LB: Outcomes from the Insulin-Only Bionic Pancreas Pivotal Extension Study

The bionic pancreas (BP) is initialized with body weight only, makes all insulin dosing decisions autonomously, and uses meal announcements without carbohydrate counting. After completing a Pivotal RCT, 90 of the 107 (aged 6-71) participants who were randomized to the 13-week baseline standard-care (SC) arm in the RCT participated in the 13-week Insulin-Only BP Pivotal Extension Study (ES) . Visit completion rate was 100%. During the baseline SC period, 1 adult participant had 2 severe hypoglycemia (SH) events; the same participant had 2 additional SH events during the ES. During the baseline SC period there were no DKA events; 1 pediatric participant had a DKA event due to infusion set failure during the ES. A decrease of HbA1c, mean CGM glucose, percent of time <70 mg/dl, percent of time >180 mg/dl, and glucose coefficient of variability (CV) , and increased percent of time in range (TIR) occurred in the Extension Study when compared to the baseline SC period. Changes from baseline were similar in adult (N=42) and pediatric (N=48) participants, except for reductions in time <70 mg/dl and CV, which were statistically significant in the pediatric but not adult sub-groups (Table) . The 90 participants who randomized to a 13-week baseline SC period and transitioned to the 13-week ES had statistically significant improvements in HbA1c and CGM outcomes following transition to the Insulin-Only BP without increases in hypoglycemia. Disclosure J. L. Lynch: Consultant; Novo Nordisk, Novo Nordisk, Novo Nordisk, Research Support; Beta Bionics, Inc., Beta Bionics, Inc., Beta Bionics, Inc., Jaeb Center for Health Research, Jaeb Center for Health Research, Jaeb Center for Health Research. Funding Funding from National Institute of Diabetes and Digestive and Kidney Diseases (grant #1UC4DK108612-01) Funding and bionic pancreas devices from Beta Bionics, Inc. Fast-acting insulin aspart and insulin aspart provided by Novo Nordisk Insulin lispro by Eli LillyBlood glucose meters and test strips (Contour?Next One Blood Glucose Monitoring System) provided by Ascensia Diabetes Care, Basel, CH. Continuous glucose monitor sensors and transmitters were purchased from Dexcom, Inc. at a discounted price.

101-LB: Utility and Safety of Backup Insulin Regimens Generated by the Bionic Pancreas—A Randomized Study

The bionic pancreas (BP) is initialized with body weight only, makes all insulin dosing decisions autonomously, and uses meal announcements without carbohydrate counting. In the event of device malfunction, BP users may need guidance for managing their glucose. The BP generates and continuously updates a table with back-up insulin doses for either injection or pump users, including a long-acting insulin dose, a four-period basal insulin profile, short-acting meal doses for breakfast, lunch, and dinner depending on meal size (Usual, Less, More) , and a glucose correction factor. At the end of the 13-week Pivotal RCT of the BP in type 1 diabetes, participants in the BP group (6-83 years old) were randomized 1:1 to their Pre-study Insulin Regimen (N=148) or BP Guidance (N=149) , and took insulin by injection or pump according to their pre-study modality, for a 2-4 day Transition Phase. There was high adherence to the regimen in the BP Guidance group and no episodes of severe hypoglycemia or DKA. Glycemic outcomes in the BP Guidance group were similar to those the Pre-study Insulin Regimen group, and comparable to their own pre-randomization baseline. Both groups had higher mean glucose and less time in range in the Transition Phase than while using the BP during the RCT (Table) . A back-up insulin regimen automatically generated by the BP can be safely implemented if need arises to discontinue use of the BP. Disclosure N. Mauras: Research Support; AbbVie Inc., Beta Bionics, Inc., Novo Nordisk, Pfizer Inc. Funding Funding from National Institute of Diabetes and Digestive and Kidney Diseases (grant #1UC4DK108612-01) Funding and bionic pancreas devices from Beta Bionics, Inc. Fast-acting insulin aspart and insulin aspart provided by Novo Nordisk Insulin lispro by Eli LillyBlood glucose meters and test strips (Contour?Next One Blood Glucose Monitoring System) provided by Ascensia Diabetes Care, Basel, CH. Continuous glucose monitor sensors and transmitters were purchased from Dexcom, Inc. at a discounted price.