Time Continuous Glucose Monitoring Research Articles

Glucose Color Index: Development and Validation of a Novel Measure of the Shape of Glycemic Variability.

Standard continuous glucose monitoring (CGM) metrics: mean glucose, standard deviation, coefficient of variation, and time in range, fail to capture the shape of variability in the CGM time series. This information could facilitate improved diabetes management. We analyzed CGM data from 141 adults with type 2 diabetes in the Hyperglycemic Profiles in Obstructive Sleep Apnea (HYPNOS) trial. Participants in HYPNOS wore CGM sensors for up to two weeks at two time points, three months apart. We calculated the log-periodogram for each time period, summarizing using disjoint linear models. These summaries were combined into a single value, termed the Glucose Color Index (GCI), using canonical correlation analysis. We compared the between-wear correlation of GCI with those of standard CGM metrics and assessed associations between GCI and diabetes comorbidities in 398 older adults with type 2 diabetes from the Atherosclerosis Risk in Communities (ARIC) study. The GCI achieved a test-retest correlation of R = .75. Adjusting for standard CGM metrics, the GCI test-retest correlation was R = .55. Glucose Color Index was significantly associated (p < .05) with impaired physical functioning, frailty/pre-frailty, cardiovascular disease, chronic kidney disease, and dementia/mild cognitive impairment after adjustment for confounders. We developed and validated the GCI, a novel CGM metric that captures the shape of glucose variability using the periodogram signal decomposition. Glucose Color Index was reliable within participants over a three-month period and associated with diabetes comorbidities. The GCI suggests a promising avenue toward the development of CGM metrics which more fully incorporate time series information.

Association of Fructosamine Levels With Glycemic Control in Children With Type 1 Diabetes as Determined by Continuous Glucose Monitoring: Results From the CGM TIME Trial

ObjectiveOur aim in this study was to determine the correlation between serum fructosamine and average blood glucose, as measured by continuous glucose monitoring (CGM) in children with type 1 diabetes. MethodsNinety-seven blood samples were collected from 70 participants in the Timing of Initiation of continuous glucose Monitoring in Established pediatric diabetes (CGM TIME) Trial. Each eligible participant had 3 weeks of CGM data with at least 60% CGM adherence before blood collection. Ordinary least-squares linear regression incorporating restricted cubic splines was used to determine the association between fructosamine levels and mean blood glucose. ResultsAn association was found between fructosamine and mean blood glucose, with an F statistic of 9.543 (p<0.001). Data were used to create a formula and conversion chart for calculating mean blood glucose from fructosamine levels for clinical use. ConclusionsThere is a complex relationship between average blood glucose, as determined by CGM and fructosamine. Fructosamine levels may be clinically useful for assessing short-term glycemic control when CGM is not available.

A Machine Learning Model for Week-Ahead Hypoglycemia Prediction From Continuous Glucose Monitoring Data.

Remote patient monitoring (RPM) programs augment type 1 diabetes (T1D) care based on retrospective continuous glucose monitoring (CGM) data. Few methods are available to estimate the likelihood of a patient experiencing clinically significant hypoglycemia within one week. We developed a machine learning model to estimate the probability that a patient will experience a clinically significant hypoglycemic event, defined as CGM readings below 54 mg/dL for at least 15 consecutive minutes, within one week. The model takes as input the patient's CGM time series over a given week, and outputs the predicted probability of a clinically significant hypoglycemic event the following week. We used 10-fold cross-validation and external validation (testing on cohorts different from the training cohort) to evaluate performance. We used CGM data from three different cohorts of patients with T1D: REPLACE-BG (226 patients), Juvenile Diabetes Research Foundation (JDRF; 355 patients) and Tidepool (120 patients). In 10-fold cross-validation, the average area under the receiver operating characteristic curve (ROC-AUC) was 0.77 (standard deviation [SD]: 0.0233) on the REPLACE-BG cohort, 0.74 (SD: 0.0188) on the JDRF cohort, and 0.76 (SD: 0.02) on the Tidepool cohort. In external validation, the average ROC-AUC across the three cohorts was 0.74 (SD: 0.0262). We developed a machine learning algorithm to estimate the probability of a clinically significant hypoglycemic event within one week. Predictive algorithms may provide diabetes care providers using RPM with additional context when prioritizing T1D patients for review.

118-LB: Feasibility of a Prototype Dual Function Glucose Sensing Insulin Delivery Cannula in People with Type 1 Diabetes

Background: SynerGTM (Pacific Diabetes Technology, Portland, OR), a prototype subcutaneous cannula combining glucose sensing and insulin infusion could reduce the burden on people with diabetes (PWD) using insulin pump therapy (IPT) and glucose sensors. Aim: To assess and refine SynerGTM sensor accuracy, evaluate insulin infusion site function, and user tolerability. Method: Adults with type 1 diabetes established on IPT participated. Following a 48-hour run-in using a commercially available infusion set with a study pump (Medtronic 780G), a SynerGTM set was inserted subcutaneously in the anterior abdomen and attached to the pump. Participants were blinded to the investigational device readings. A standardised meal was eaten 60 minutes post-insertion. Glucose was measured on arterialised blood by YSI and finger-stick test strips (Roche Accu-Chek Guide) every 10 minutes for 60 minutes before and every 15 minutes for 4 hours after the meal. An insulin bolus was delivered immediately prior to the study meal using SynerGTM. Insulin continued to be delivered using SynerGTM at home and fingerstick readings continued. A second meal test as described above was performed on day 4 before study conclusion. A commercially available (Dexcom G6) continuous glucose monitor (CGM) was inserted prior to the 48 hour run-in preceding day 1 and worn throughout the study. Result: In 15 participants 99% of 679 data pairs of SynerGTM vs. YSI and 1305 data pairs vs. capillary glucose measurement were in zones A and B of the consensus error grid. MARD was 13.5% and 13.5% respectively. No artifact sensor spikes were observed with bolused insulin. SynerGTM was well tolerated and there were no significant differences in CGM time in range or TDD of insulin compared with run-in. Conclusion: This study proves the feasibility of SynerGTM as the first single through-the-skin device providing co-located insulin infusion and glucose sensing for PWD who take insulin. Disclosure J. Lu: None. C. Yuan: None. Y. W. Kong: None. E. Netzer: None. H. Wu: None. T. Seidl: None. D. N. O'neal: None. Funding JDRF (IND-2016-261)

978-P: Correlation between Continuous Glucose Monitoring Metrics, Oral Glucose Tolerance Test, and HbA1c in Individuals with Impaired Glucose Tolerance

Aims: Prediabetes is defined by HbA1c, fasting (FPG) and 2-hour plasma glucose (2h-PG) during 75 gram oral glucose test (OGTT) although these values often show poor concordance. Most diabetes prevention trials involved people with impaired glucose tolerance (IGT) diagnosed as 2h-PG 7.8-11.0 mmol/L. Continuous glucose monitoring (CGM) may detect early dysglycemia especially postprandial glucose excursions. We examined correlations amongst HbA1c, FPG, 1-h and 2h-PG during OGTT and CGM metrics in individuals with IGT. Methods: 85 Chinese with IGT (mean±SD age 57±8 years, 51 (60%) female, BMI 26.6±4.0 kg/m2) participating in a lifestyle modification study had measurement of HbA1c, PG during 75g OGTT and 14-day CGM metrics (Freestyle Libre, Abbott) during a 3-week period at baseline. We examined their correlations using Spearman coefficients. Results: The mean HbA1c was 5.8±0.3 %, FPG 5.3±0.4 mmol/L, 1h-PG 11.0±1.6 mmol/L and 2h-PG 8.5±1.3 mmol/L. For CGM, the mean glucose management index (GMI) was 5.9±0.2 % and time in tight glycemic range (TITR 3.9-7.8 mmol/L) was 86.7±6.6 %. There was a tendency for HbA1c (r=0.587, p=0.097) to correlate with GMI and negatively with TITR (r=-0.644, p=0.061). The respective correlates between FPG and GMI was r=0.623 (p=0.073) and that between FPG and TITR was r=-0.559 (p=0.118). There was weak correlation between 1h-PG and CGM time above range &amp;gt;7.8 mmol/L (r=0.301, p=0.43). There was no correlation amongst 1h-PG, 2h-PG with HbA1c and other conventional CGM metrics. Conclusions: In individuals with IGT, HbA1c and FPG showed better correlations with CGM metrics of average glycemia than PG values during OGTT. Future studies are needed to define the use of CGM and appropriate metrics in detection of IGT. Disclosure E.Chow: Research Support; Medtronic, Merck KGaA, Speaker's Bureau; Novartis, Bayer Inc., Sanofi. J.He: None. N.Chu: None. E.W.M.Poon: None. J.C.Chan: Board Member; Asia Diabetes Foundation, Consultant; Bayer Inc., Celltrion, Boehringer Ingelheim and Eli Lilly Alliance, Sanofi, Research Support; AstraZeneca, Servier Laboratories, Viatris Inc., Hua Medicine, Merck KGaA, Applied Therapeutics Inc., Lee Powder, Pfizer Inc., Speaker's Bureau; Novartis, Stock/Shareholder; GemVCare Ltd. Funding Health and Medical Research Fund (17180431)

19-OR: Monitoring Stage 2 to Stage 3 Transition in Type 1 Diabetes Using Continuous Glucose Monitoring (CGM)

Introduction: CGM can be used to monitor evolution of dysglycemia in presymptomatic, stage 2 type 1 diabetes (T1D) and to target early initiation of insulin upon reaching stage 3 T1D. Methods: Participants (N=31) with dysglycemia by A1c, OGTT, CGM and/or home glucose testing (HGT) measures (Table 1) were randomized 2:1 into 6 months unblinded CGM-guided education intervention vs blinded CGM and monitoring only. Surveys included PHQ8, PedsQLFamilyImpact, Tolerance of Uncertainty and Diabetes Knowledge (30 point scale). Results: Intervention and control group were similar in baseline characteristics (Table), except younger age and significantly higher 2-hour OGTT glucose level in the intervention group. Diabetes knowledge was not significantly different between intervention and control at baseline (22±6 vs 20±7, p=0.61) or 3-month follow-up (23±6 vs 20±4, p=0.28). There were no differences between groups for PHQ8, PedsQLFamilyImpact or Tolerance of Uncertainty at baseline or follow-up to date. Of those diagnosed with stage 3 T1D, CGM average sensor glucose and time &amp;gt;140 mg/dL were higher than at study entry. All participants met ADA stage 3 diagnostic criteria; however, none had A1c≥6.5%. Conclusion: CGM facilitates early recognition of stage 3 T1D before elevation in HbA1c and metabolic decompensation. Unblinded CGM does not appear to negatively impact quality of life. Disclosure B.I.Frohnert: Advisory Panel; Provention Bio, Inc. F.Sepulveda: None. J.E.Stoughton: None. A.Steck: None. K.M.Simmons: Advisory Panel; Provention Bio, Inc., Consultant; Dexcom, Inc., Provention Bio, Inc., Research Support; Novartis. Funding The Leona M. and Harry B. Helmsley Charitable Trust (G-1911-03463)

230-OR: Hybrid Continuous Glucose Monitoring Protocols in the Intensive Care Unit—Multicenter Experience

There is limited experience with continuous glucose monitoring (CGM) in intensive care units (ICUs). We assessed the effectiveness, safety, and accuracy of real-time CGM use in COVID-19 ICUs. We pooled data from three academic centers using hybrid protocols combining point of care (POC) blood glucose (BG) with non-adjunctive CGM. At hospitals A and B, CGM was used non-adjunctively with POC at least every 6 hours. At hospital C, POC BG was performed every two hours. We also examined CGM performance during lowest pAO2, oxygen saturation, pH, and mean arterial pressure. Of 169 patients, mean age was 60.9±12.2 years, and 82%, 93%, and 62% received corticosteroids, mechanical ventilation, and vasopressors respectively. Median duration of IV insulin was 118, 156, and 50 hours at hospitals A, B, and C respectively. Mean POC BG frequency was 10.2±6.1, 7.0±5.2, and 16±6.5 times/day on day 1 of IV insulin and 6.7±3.1, 6.0±3.5, and 12.2±5.8 times/day thereafter. The median percent CGM time in range (TIR, 70-180mg/dl) was 72%, 70% and 46% during IV insulin. Median time &amp;lt;70mg/dl was &amp;lt;0.1% for all hospitals. The absolute relative difference between CGM and POC did not correlate with hemodynamic instability, Figure. This data indicates hybrid monitoring can reduce POC frequency while safely maintaining glucose. Sensor-meter agreement was not associated with abnormal physiologic parameters and requires further study. Disclosure E.R.Faulds: Advisory Panel; Dexcom, Inc., Other Relationship; A1Control, LLC, Research Support; Dexcom, Inc., Speaker's Bureau; Dexcom, Inc., Medscape. M.Garcia: None. S.Chandra: None. F.J.Pasquel: Consultant; Dexcom, Inc., Medscape, Research Support; Dexcom, Inc., Ideal Medical Technologies. K.M.Dungan: Board Member; Elsevier, Consultant; Eli Lilly and Company, Dexcom, Inc., Other Relationship; UpToDate, Research Support; Dexcom, Inc., Abbott, ViaCyte, Inc., Sanofi, Speaker's Bureau; Academy for Continued Healthcare Learning, Cardiometabolic Health Congress, Medscape, Integritas. Y.Badakhshi: None. A.Boutsicaris: None. M.C.Exline: Other Relationship; Abbott Nutrition. J.Hester: None. L.G.Jones: None. M.Mcnett: Research Support; Dexcom, Inc., National Institutes of Health. J.D.Miller: Advisory Panel; MannKind Corporation, Employee; Medtronic, Research Support; Dexcom, Inc. R.Shah: None.

Glycemia Risk Index as a novel metric to evaluate the safety of glycemic control in children and adolescents with Type 1 Diabetes: an observational, multicenter, real-life cohort study.

Glycemia Risk Index (GRI) is a novel composite metric for the evaluation of the safety of glycemic management and control. The aim of this study was to evaluate GRI and its correlations with continuous glucose monitoring (CGM) metrics by analysing real-life CGM data in 1067 children/adolescents with type 1 Diabetes (T1D) using four different treatment strategies (intermittently scanned CGM [isCGM]-Multiple daily injections [MDI]; real time CGM [rtCGM]-MDI; rt-CGM-insulin pump [IP]; Hybrid Closed-Loop [HCL] therapy). GRI was positively correlated with High Blood Glucose Index (HBGI), Low Blood Glucose Index (LBGI), mean glycemia, its standard deviation, coefficient of variation and HbA1c. The four treatment strategies groups showed significantly different GRI with the lowest value in the HCL group (30.8) and the highest in the isCGM-MDI group (68.4). These findings support the use of GRI for the assessment of the glycemic risk and the safety of specific treatment in paediatric subjects with T1D.

Update information on type 1 diabetes in children/adolescents and adults.

Update information on type 1 diabetes in children/adolescents and adults.

The Diabetes teleMonitoring of patients in insulin Therapy (DiaMonT) trial: study protocol for a randomized controlled trial

BackgroundThe effect of telemedicine solutions in diabetes remains inconclusive. However, telemedicine studies have shown a positive trend in regards to glycemic control. The telemedicine interventions that facilitate adjustment of medication seems to improve glycemic control more effectively. Hence, it is recommended that future telemedicine studies for patients with diabetes include patient-specific suggestions for changes in medicine. Hence, the aim of the trial is to explore the effect of telemonitoring in patients with type 2 diabetes (T2D) on insulin therapy.MethodsThe trial is an open-label randomized controlled trial with a trial period of 3 months conducted in two sites in Denmark. Patients with T2D on insulin therapy will be randomized (1:1) to a telemonitoring group (intervention) or a usual care group (control). The telemonitoring group will use a continuous glucose monitor (CGM), an insulin pen, an activity tracker, and smartphone applications throughout the trial. Hospital staff will monitor the telemonitoring group and contact the subjects by telephone repeatedly throughout the trial period. The usual care group will use a blinded CGM the first and last 20 days of the trial and will use a blinded insulin pen for the entire period.The primary endpoint will be changed from baseline in CGM time in range (3.9–10.0 mmol/L) 3 months after randomization. Secondary endpoints include change from baseline in glycated hemoglobin (HbA1c), total daily dose, time above range, and time below range 3 months after randomization. Exploratory endpoints include health-related quality of life, diabetes-related quality of life, etc.DiscussionThe DiaMonT trial will test a telemonitoring setup including various devices. Such a setup may be criticized, because it is impossible to determine which element(s) add to the potential effect. However, it is not possible and counterproductive to test the elements individually, since it is the full telemedicine setup that is being evaluated. The DiaMonT trial is the first Danish trial to explore the effect of telemonitoring on patients on insulin therapy. Thus, the DiaMonT trial has the potential to form the basis for the implementation of telemedicine for patients with T2D in Denmark.Trial registrationClinicalTrials.gov NCT04981808. Registered on 8 June 2021.

ISPAD Clinical Practice Consensus Guidelines 2022: Glycemic targets and glucose monitoring for children, adolescents, and young people with diabetes.

ISPAD Clinical Practice Consensus Guidelines 2022: Glycemic targets and glucose monitoring for children, adolescents, and young people with diabetes.

Glucose profiles of older adults with type 1 diabetes using sensor-augmented pump therapy in Australia: pre-randomisation results from the ORACL study

Older adults with type 1 diabetes are recommended modified glucose targets. However, data on the effects of diabetes technology in older age are scarce. We assessed older adults established on sensor-augmented insulin pump therapy during clinical trial run-in and compared their continuous glucose monitoring (CGM) profiles with consensus recommendations. We aimed to provide insight into the applicability of currently recommended CGM-based targets while accounting for current Diabetes UK guidelines. In this analysis, adults aged 60 years or older with type 1 diabetes with a duration of at least 10 years and entering the Older Adult Closed Loop (ORACL) trial were studied. The trial was done at two tertiary hospitals in Australia. Individuals who were independent with diabetes self-management, as well as those receiving caregiver assistance for their diabetes management, were eligible for inclusion. Participants underwent baseline clinical assessment, which included medical history and examination, testing for frailty, functional ability, cognitive functioning, psychosocial wellbeing, and subjective sleep quality; fasting venous blood samples were collected for C-peptide, glucose, and glycated haemoglobin A1c measurement. Sensor-augmented pumps, carbohydrate-counting education, and diabetes education were provided to participants by diabetes nurse educators, dietitians, and endocrinologists experienced in type 1 diabetes clinical care. CGM data were subsequently collected for 2 weeks during sensor-augmented pump therapy. The ORACL trial is registered with the Australian New Zealand Clinical Trial Registry, ACTRN12619000515190. Our analysis included all 30 participants who completed the ORACL trial run-in-19 (63%) women and 11 (37%) men (mean age 67 years [SD 5], median diabetes duration 38 years [IQR 20-47], and insulin total daily dose 0·55 units [0·41-0·66] per kg bodyweight). Ten (33%) of 30 participants had impaired hypoglycaemia awareness and six (20%) were pre-frail; none were frail. The median CGM time in range 3·9-10·0 mmol/L was 71% (IQR 64-79). The time spent with glucose above 10·0 mmol/L was 27% (18-35) and above 13·9 mmol/L was 3·9% (2·4-10·2). The time with glucose below 3·9 mmol/L was 2·0% (1·2-3·1) and the time below 3·0 mmol/L was 0·2% (0·1-0·4). Only two (7%) of 30 participants met all CGM-based consensus recommendations modified for older adults. Time in hypoglycaemia was lower among the 16 participants with predictive low-glucose alerts enabled than among the 14 participants not using predictive low-glucose alerts (median difference -1·1 percentage points [95% CI -2·0 to -0·1]; p=0·038). This difference was even greater overnight (-2·3 percentage points [-3·2 to -1·0]; p=0·0018). One serious adverse event occurred (elective cardiac stent). Using sensor-augmented pumps after multidisciplinary education, this group of older adults without frailty achieved a time in range far exceeding minimum consensus recommendations. However, the current stringent hypoglycaemia recommendations for all older adults were not met. Predictive low alerts could reduce hypoglycaemia, particularly overnight. Investigation into the effectiveness of CGM-based targets that consider frailty, functional status, and diabetes therapies for older adults is warranted. JDRF and Diabetes Australia.

Key updates to the management of type 2 diabetes in adults: how to embrace and embed the new NICE guidance

Key updates to the management of type 2 diabetes in adults: how to embrace and embed the new <scp>NICE</scp> guidance

Continuous Glucose Monitoring Time Series Data Analysis: A Time Series Analysis Package for Continuous Glucose Monitoring Data.

The R package Continuous Glucose Monitoring Time Series Data Analysis (CGMTSA) was developed to facilitate investigations that examine the continuous glucose monitoring (CGM) data as a time series. Accordingly, novel time series functions were introduced to (1) enable more accurate missing data imputation and outlier identification; (2) calculate recommended CGM metrics as well as key time series parameters; (3) plot interactive and three-dimensional graphs that allow direct visualizations of temporal CGM data and time series model optimization. The software was designed to accommodate all popular CGM devices and support all common data processing steps. The program is available for Linux, Windows, and Mac at GitHub.

Continuous Glucose Monitoring Metrics and Birth Weight: Informing Management of Type 1 Diabetes Throughout Pregnancy.

To determine gestational weekly changes in continuous glucose monitoring (CGM) metrics and 24-h glucose profiles and their relationship to infant birth weight in pregnant women with type 1 diabetes. An analysis of >10.5 million CGM glucose measures from 386 pregnant women with type 1 diabetes from two international multicenter studies was performed. CGM glucose metrics and 24-h glucose profiles were calculated for each gestational week, and the relationship to normal (10-90th percentile) and large (>90th percentile) for gestational age (LGA) birth weight infants was determined. Mean CGM glucose concentration fell and percentage of time spent in the pregnancy target range of 3.5-7.8 mmol/L (63-140 mg/dL) increased in the first 10 weeks of pregnancy and plateaued until 28 weeks of gestation, before further improvement in mean glucose and percentage of time in range until delivery. Maternal CGM glucose metrics diverged at 10 weeks of gestation, with significantly lower mean CGM glucose concentration (7.1 mmol/L; 95% CI 7.05-7.15 [127.8 mg/dL; 95% CI 126.9-128.7] vs. 7.5 mmol/L; 95% CI 7.45-7.55 [135 mg/dL; 95% CI 134.1-135.9]) and higher percentage of time in range (55%; 95% CI 54-56 vs. 50%; 95% CI 49-51) in women who had normal versus LGA. The 24-h glucose profiles were significantly higher across the day from 10 weeks of gestation in LGA. Normal birth weight is associated with achieving significantly lower mean CGM glucose concentration across the 24-h day and higher CGM time in range from before the end of the first trimester, emphasizing the need for a shift in clinical management, with increased focus on using weekly CGM glucose targets for optimizing maternal glycemia from early pregnancy.

32-LB: Role of Telehealth in Improving Health Care Quality in Type 2 Diabetes in a Rural Community-Based Clinical Practice

Use of continuous glucose monitoring (CGM) facilitates better management of type 2 diabetes (T2D) in outpatient clinics in academic medical centers. In a Quality Improvement project, we evaluated the effects of telehealth aided by CGM on HbA1c and percent time in range (TIR 70-180 mg/dl) in patients with T2D over a 6-month duration. Diabetes management advice based on intermittent CGM use was provided via phone by an academic endocrinologist to the clinical pharmacist at a rural community clinic. Inadequately controlled (HbA1c &amp;gt; 8%) T2D patients (n=27) on at least one antidiabetic drug were included and were monitored over a 6 month period, baseline (V1) , 3 months (V2) and 6 months (V3) . All patients received standard of care (SOC) treatment. On each visit, 12 patients (age 59 8y, BMI 36.2 3.6kg/m2, HbA1C 9.3 1.3%) received a professional CGM for 2 weeks. Thereafter, the devices were removed and analyzed. The PCP implemented medication changes based on recommendations by the endocrinologist to the clinical pharmacist. The other 15 patients (age 60 8y, BMI 32 4 kg/m2, HbA1C 9.1 1.6%) underwent the same schedule without CGM or telehealth consultations. There were no significant differences in age or HbA1c between groups at baseline. HbA1c (%) decreased (Δ -1.0 ± 1.4; p=0.03) , and CGM TIR (%) increased (Δ +24.3 ± 38.7; p=0.05) at V3 vs. V1 in the telehealth group. However, weight, CGM time below and above ranges (%) did not differ (p&amp;gt;0.05) statistically. In contrast, there were no significant differences in HbA1c (Δ +0.4 ± 1.7; p=0.3) between V3 and V1 in the SOC without telehealth group. Our results show that intermittent CGM use coupled with tele-consultation with diabetes expert improves glucose control and care quality in patients with T2D in community based health practice. This strategy could potentially mitigate existing health care disparities between suburban/rural-based community health practices and academic medical centers, thus closing the gaps in quality of care. Disclosure S. Gururaj: None. A. Asfa: None. L. C. Hanley: None. Y. R. Yadav: None. R. T. Bashore: None. R. Basu: Consultant; Sparrow Pharmaceuticals Inc, Research Support; Abbott Diabetes, AstraZeneca. A. Basu: Speaker’s Bureau; Zealand Pharma A/S. Funding Virginia Department of Health (GS11963)

94-LB: A Phase 1, Double-Blind, Placebo-Controlled Multiple Escalating Dose Study of RGT-075 Novel Small-Molecule Oral GLP-1 Receptor Agonist in Adults with Type 2 Diabetes

Background: RGT-075 is a small-molecule oral GLP-1 receptor agonist (GLP1RA) in clinical development. RGT-075 (15-280 mg) was well-tolerated by healthy adults in a prior phase 1 single ascending dose study. Phase 1 multiple-ascending dose study of RGT-075 in adults with type 2 diabetes (T2DM) results are presented. Methods: A Phase 1, randomized, double-blind, placebo-controlled, inpatient study planned multiple daily doses of RGT-075 or matching placebo (6 active/2 placebo per cohort) for up to 28 days in T2DM patients (baseline HbA1c 6-10.5% and concomitant metformin &amp;gt;500 mg/d) . An adaptive study design permitted dose and titration adjustments. Assessments included safety (primary) , PK, and exploratory efficacy changes in HbA1c, fasting plasma glucose (FPG) , continuous glucose monitoring time in range (CGM-TIR) , mixed-meal tolerance test MMTT, and body weight (BW) . Results: Four Cohorts enrolled 36 patients (56%F/44%M; mean age 55yF/61yM) to receive ([Starting Dose]→[Titration Duration]→[Target Dose]) : Cohort 1 [60 mg][ no titration][60 mg]; Cohort 2 [30 mg][10 d][120 mg]; Cohort 3 [15 mg][20 d][180 mg]; Cohort 4 [15 mg][28 d][45 mg]. Adverse events (AEs) were reported by most subjects (100% C1 + C2; 88% C3; 83% C4; 78% pooled placebo) . Most AEs were of mild severity, drug-related for RGT-075 groups, and predominantly GI with no serious AEs or deaths. Baseline HbA1c was highly variable (7.2-9.0%) ; HbA1c mean changes from baseline ranged -0.6 to -1.2% across RGT-075 doses vs. -0.37% with placebo and correlated with FPG, CGM-TIR and MMTT results. BW mean change ranged -2.3 to -4.5 kg across RGT-075 doses vs -1.1 kg with placebo. RGT-075 plasma exposures increased as the dose levels increased. Conclusion: RGT-075 GLP1RA oral small molecule (15-180 mg/day) up to 28 days was safe and QD dosing showed promising exploratory efficacy trends despite high baseline variability and short treatment duration. Disclosure M. A. Pirner: Employee; Regor Pharmaceuticals, Inc. J. Lin: None. F. Liu: Employee; Regor Pharmaceuticals Inc. L. Yao: None. M. E. Zettler: Employee; Cardinal Health, Regor Pharmaceuticals Inc. D. J. Valacer: Employee; Regor Pharmaceuticals, Inc.

253-OR: Maternal Hyperglycemia Is Associated with Offspring Adiposity through Altered Lipid Metabolism in Type 1 Diabetes Pregnancy

Objective: Type 1 diabetes in pregnancy is associated with increased birthweight and adiposity in offspring, which may contribute to increased future cardiometabolic risk. We assessed if the effects of maternal hyperglycemia on adiposity were mediated by altered lipid metabolism in pregnancy. Methods and Outcomes: 200 women in the Continuous Glucose Monitoring (CGM) in Pregnant women with Type 1 diabetes Trial (CONCEPTT) had a livebirth. Liquid chromatography-mass spectrometry of maternal serum (12, 24, 34 wks; n=174) and cord blood (n=93) measured 21metabolites. Anthropometry was performed at birth by trained staff. Statistical Analysis: Linear regression of CGM time above range 12, 24, 34 wks (&amp;gt;140 mg/dl; &amp;gt;7.8 mmol/l) with adjustment for maternal age, BMI, parity, ethnicity, education and intervention (birth gestation for offspring) . Significance limit was p=0.0for unselected analysis of all metabolites, p=0.for subset analysis with skinfolds. Results: Maternal hyperglycemia at 24 and 34 weeks was associated with increased triglycerides containing FA (16:0) , (16:1) , (18:0) and (18:1) in maternal serum, considered biomarkers of de novo lipogenesis. Maternal hyperglycemia was associated with abundant carnitines in cord blood, suggesting increased fetal beta oxidation. Lipogenesis-associated species (maternal serum) and carnitines (cord blood) were associated with offspring skinfold sum, independently of maternal hyperglycemia. However, maternal hyperglycemia retained a strong independent association with skinfold sum despite adjustment for potential lipid mediators. Conclusions: Maternal hyperglycemia is associated with features of de novo lipogenesis in the maternal metabolism and beta oxidation in the offspring. Altered lipid metabolism significantly contributes to offspring adiposity but does not appear to solely mediate the relationship between hyperglycemia and body composition in type 1 diabetes pregnancy. Disclosure C.L.Meek: Research Support; Dexcom, Inc. Z.A.Stewart: None. D.Feig: Advisory Panel; Novo Nordisk, Research Support; Apotex. A.Koulman: None. H.R.Murphy: Advisory Panel; Medtronic. Funding This project was funded by Diabetes UK (PG 2017/2278) . The CONCEPTT trial was funded by Juvenile Diabetes Research Foundation (JDRF) grants #17-2011-533, and grants under the JDRF Canadian Clinical Trial Network, a public-private partnership including JDRF and FedDev Ontario and supported by JDRF #80-2010-585. Medtronic supplied the CGM sensors and CGM systems at reduced cost. The study sponsor/funders were not involved in the design of the study; the collection, analysis, and interpretation of data; writing the report; and did not impose any restrictions regarding the publication of the report.CLM is supported by the Diabetes UK Harry Keen Intermediate Clinical Fellowship (DUK-HKF 17/0005712) and the European Foundation for the Study of Diabetes – Novo Nordisk Foundation Future Leaders’ Award (NNF19SA058974) . HRM conducts independent research supported by the National Institute for Health Research (Career Development Fellowship, CDF-2013-06-035) , and is supported by Tommy’s charity. SF and AK acknowledge funding from the BBSRC (BB/M027252/1) . DSF conducts independent research supported by the Canadian Institute for Health Research.

702-P: Short-Period CGM Data Are More Useful than HbA1c for Treatment Adjustment in Type 2 Diabetes Patients

Current recommendations for type 2 diabetes treatment are based on targeting HbA1c depending on different factors. HbA1c reflects recent 8-12 weeks glucose control, but amount of data show HbA1c insufficiencies. Continuous glucose monitoring (CGM) is used for insulin dose adjustment but also for control assessment and treatment determination. In our study we compare seven-day non-real time CGM data and HbA1c to find out which is more appropriate for determining drug prescription in patients with type 2 diabetes. Our study includes 85 patients with type 2 diabetes (35 women, 50 men; age 43.93±10.87 years, disease duration 21.91±6.years) , on different treatment regimens (31 on oral therapy, 33 treated with premixed insulin, 21 on multiple insulin injections) . Patients performed Non-Real Time Continuous Glucose Monitoring by using iProTM for seven days and HbA1c was measured at the end of this period. No difference in HbA1c level was found in three observed groups. HbA1c showed moderate negative correlation to percentage of time within the limits (r1=-0.48; r2= -0.654; r3= -0.674) . Based on HbA1c assessment 34.84% of type 2 patients on oral medications, 36.36% of patients on premixed insulin and 19.05% of type 2 patients on multiple daily injection are with good control. CGM defined 54.84% of type 2 patients on oral medication, 18.27% of patients on premixed insulin and 17.80% of type 2 patients on intensified regimen as well controlled. CGM was more precise than HbA1c in defining patients with good control (p&amp;lt;0.05) . HbA1c shows bad correlation with overall glucose excursion (r1=-0.096, r2=0.200, r3=0.377) . No difference was found between studied groups in regard to CGM percentage of time spent above and below the limits and to AUC above upper limit. We conclude that CGM data give more precise information than HbA1c about glucose control and are more useful for defining treatment adjustment. Even performing CGM for short period helps physician to choose the most appropriate drug. Disclosure T.Totomirova: n/a. M.Arnaudova: None.

1032-P: Improving CGM Uptake in Minority Youth and Youth with Poorly Controlled T1D

Background: Continuous glucose monitoring (CGM) improves glycemic control for youth with type 1 diabetes (T1D) . Despite these benefits, less than half of youth with T1D use CGM. Objective: To determine if trial CGM use (10-days) increases CGM uptake and is associated with improved glycemic control among minority youth and youth with poorly controlled T1D. We also examined barriers to CGM use. Methods: This prospective study was conducted at an academic Pediatric Diabetes Center. Youth with T1D for more than 3 months, with no previous CGM usage, or no CGM usage in the last year were enrolled. Diabetes staff placed CGM at the point of care (POC) and provided CGM education. Barriers to prior CGM use, hemoglobin A1c (HbA1c) , and demographic information were recorded. Participants received 5 and 10-day follow up calls from the diabetes team to review glycemic trends. At 3 months, participants' use of CGM and HbA1c were recorded. Results: Youth with T1D (n=22) were enrolled (13 first time CGM users, 9 past users) , mean age 14.1years (SD 3.1) , 41% non-Hispanic black, 68% female, mean diabetes duration 6.3 ± 4.4years, and baseline mean HbA1c of 10.8%. Patient-cited barriers to prior CGM use included technical difficulties (n=5) , unawareness of CGM (n=4) , difficulty obtaining CGM (n=4) , general apprehension (n=3) , not wanting devices (n=3) , and pain, fear of needles, and new diabetes diagnosis (n=1 each) . Of participants, 20 completed some follow-up, and 18/20 (90%) cited wanting to use CGM long-term. Of 13 participants who completed 3 and/or 6-month follow-up visits, only 9 were actively using CGM. Further barriers to use included insurance issues (n=2) and CGM falling off (n=1) . There was no change in mean HbA1c from baseline to follow-up (10.8 ±2.4% vs. 10.3 ±2.3%, p=0.46) . Conclusion: There are many barriers to CGM uptake in youth. Providing a trial use of CGM at the POC may increase uptake of CGM use in at-risk diabetes populations, but further investigation of additional barriers that impact long-term adherence to CGM is needed. Disclosure T.L.Lin: None. J.A.Manfredo: None. N.Illesca: None. K.Abiola: None. N.Hwang: None. M.Seel: None. E.A.Brown: None. R.M.Wolf: Consultant; NEMA Research, Research Support; Dexcom, Inc. Funding Johns Hopkins Children's Center Innovation Award, and Dexcom