ResearchPad - diabetes-technology Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[SAT-649 Comparison of the Accuracy and Concordance of 3 CGM Devices vs SMBG During Aerobic Exercise]]> Introduction: Real-time continuous glucose monitoring (rt-CGM) and flash glucose monitoring (FGM) devices have become important tools for managing type 1 diabetes. These devices are approved for management decisions in steady-state conditions, however there is a decline in accuracy during aerobic exercise with respect to MARD and lag time.1 It is possible that newer technologies may be superior to previous devices.

Question: With the newest rtCGM, FGM, and long-term CGM devices, do we continue to see an increase in MARD during continuous aerobic exercise? Is there a difference between glucose readings of the 3 devices when worn simultaneously during exercise?

Design: A single subject with T1DM, experienced in glucose management during exercise, wore 3 devices simultaneously - the DEXCOM G6 (San Diego, CA; rt-CGM1, worn on the abdomen), the Eversense (Germantown, DM; long-term CGM or rt-CGM2, implanted in the left arm), and the Abbott Freestyle Libre 14-day (Chicago, IL; FGM, worn on the right arm). The rt-CGM2 was calibrated using a blood glucose meter (Ascensia Contour Next) which was also used for comparator SMBG. Glucose was recorded 10 minutes before and after exercise and every 10 minutes during a 60 minute run at moderate intensity. 6 exercise sessions were averaged for data analysis. Subject wore an insulin pump and reduced the basal rate by 50% 90 minutes prior to exercise and resumed the basal immediately post-exercise. Carbohydrates were not used within 3 hours prior to exercise but could be consumed during exercise if needed to avoid hypoglycemia.

Results: Glucose value during 60 minutes of exercise dropped from mean of 167 to 114 mg/dL with SMBG, 174 to 115 mg/dL with rt-CGM, 175 to 115 with rt-CGM2, and 150 to 106 mg/dL with FGM. Average measured glucose was 140.0, 145.8, 145.6, and 129.3 mg/dL for SMBG, rt-CGM1, rt-CGM2, and FGM respectively. P-value <0.05 for FGM. MARD (calculated compared to SMBG) for 10 minutes pre-exercise, during exercise, and post-exercise for rt-CGM1 was 5.1%, 11.7%, and 8.6% respectively. For rt-CGM2 MARD was 7.7%, 11.4%, and 10.0% respectively. For FGM, MARD was 12.7%, 5.3%, and 21.3% respectively. Overall MARD was 9.8% for rt-CGM1, 10% for rt-CGM2, and 8.0% for FGM.

Conclusions: Blood glucose values dropped with aerobic exercise with observed lag between CGM and SMBG. Rt-CGM1 and Rt-CGM2 showed increased MARD vs SMBG during exercise. Interestingly, lower MARD was seen for FGM during aerobic exercise likely due to bias towards lower glucose levels at baseline as reported by FGM. There was no significant difference seen during exercise between rt-CGM1 and rt-CGM2 despite the differing location of the sensors (transdermal vs subcutaneous) and method of glucose analysis (glucose oxidase vs fluorescence).

References: (1) Zaharieva et al. Diabetes Technol Ther 2019; 21: 313-321.

<![CDATA[SAT-636 The Fast-Evolving Connected Diabetes Care Landscape: Transforming Diabetes Care with Telehealth and Technology]]> Background and Aims

Recent years have brought about a new form of “connected diabetes care,” defined as digital diabetes management systems based around (1) smartphone apps, (2) devices with built-in connectivity, and (3) remote human and automated coaching and support. Given their potential to help improve health outcomes, the rapid pace of innovation, and the dearth of information about them to guide patients, providers, and payers, we provide an update on the landscape of and trends in connected diabetes care offerings.


Prominent connected diabetes care providers that have published results are categorized and characterized. Similarities and differences are identified and the state of available evidence is evaluated.


Connected diabetes care offerings were analyzed for items including: health conditions managed, care team composition, connected medical devices, and evidence. We expect these players will further expand offerings across chronic conditions, strive to integrate more deeply with the traditional healthcare system, deploy greater automation to promote scalability, and find clever ways to promote and support the use of continuous glucose monitoring in type 2 diabetes. Future evidence generation for this field should have more standardized methodology.


The field of connected diabetes care has tremendous potential to improve outcomes, but it is in its infancy in terms of awareness, uptake, and effectiveness. Further, questions regarding offerings’ abilities to support most people with diabetes sustainably remain. However, existing evidence is sufficient to support further exploration and refinement of the model as the next step in team-based diabetes care.

<![CDATA[SAT-640 Glycemic Profile of Intravenous Glucocorticoid Induced Hyperglycemia Using Continuous Glucose Monitoring]]> Background: Intravenous (IV) steroids are widely used in critically ill patients and with chemotherapy. It is well known that glucocorticoid-induced hyperglycemia (GCIH) occurs within 3 hours following oral administration of steroids with typical postprandial glycemic excursions lasting 24-36 hours. The recent increased availability of Continuous Glucose Monitoring (CGM) has allowed a detailed description of glycemic fluctuations in patients receiving steroids in different settings, however there is no reported observation of CGM findings following a single dose of IV Dexamethasone in a patient with diabetes. We present a case of glycemic pattern documented on CGM of a patient with type 2 diabetes, who had received 11 cycles of a single dose Dexamethasone-containing chemotherapy. Clinical Case: The patent is 70 years old female with history of type 2 DM of 19 years duration and metastatic pancreatic adenocarcinoma, diagnosed in November 2018, and treated with Fluorouracil and Dexamethasone 6mg IV on every other Wednesday since December 2018. Her diabetes was fairly controlled on Metformin, Repaglinide, Pioglitazone and Detemir insulin. Premeal Lispro was added while Metformin and Repaglinide were discontinued with the beginning of chemotherapy. She started using Freestyle Libre CGM in January 2019. During her visit in March 2019, the patient was taking Detemir Insulin 50 units in AM and 30 units at night, and Lispro 15 units before meals, in addition to correction insulin based on an Insulin Sensitivity Factor (ISF) of 1:25 for Blood Glucose (BG) above 200mg/dl. Unlike the reported postprandial hyperglycemic excursions associated with oral steroids, the patient’s CGM data showed a reproducible triphasic glycemic pattern following IV Dexamethasone, consisting of a steady state of hyperglycemia reached within 3 hours and lasting around 18-30 hours, followed by a transient BG improvement for 18-20 hours, and ending with another hyperglycemic plateau of 10-16 hours on day 3 post chemotherapy, with no association to meal intake. Given this recurrent pattern, the patient was advised to increase her bedtime Detemir insulin from 30 to 45 units and her correction ISF from 1:25 to 1:18 on days 1 and 2 after chemotherapy, with subsequent attenuation and shortening of GCIH. Conclusion: Our case report is the first one to describe CGM documented glycemic profile following a single dose of IV Dexamethasone in a patient with type 2 diabetes treated with insulin. The CGM data reveals a consistent steady GCIH, lasting around 48 hours, and reflecting the prolonged action of Dexamethasone. The transient BG improvement seen on day 2 is likely due to the Detemir dose self-increase and the carbohydrates intake decrease in response to day 1 hyperglycemia. A 48 hours modified insulin regimen based on higher dose of long acting and correction insulin improved Dexamethasone induced hyperglycemia.

<![CDATA[OR30-01 Real-World Minimed™ 670G System Use and Glycemic Outcomes of Pediatric and Adult Individuals Living with Type 1 Diabetes (T1D) in the United States]]> Introduction: The MiniMed™ 670G system was FDA-approved in 2016 for adults and adolescents ≥14yrs, and in 2018 for children ages 7-13yrs with T1D. Since then, use of the system has grown to over 180,000 people in the U.S. The glycemic control benefits of real-world MiniMed™ 670G system Auto Mode use in the U.S. were assessed. Methods: System data (aggregated five-minute instances of sensor glucose [SG]) uploaded from March 2017 to July 2019 by individuals (N=118,737) with T1D and ≥7yrs of age who enabled Auto Mode were analyzed to determine the mean % of overall time spent <54mg/dL/<70mg/dL (TBR); between 70-180mg/dL (TIR); and >180mg/dL/>250mg/dL (TAR). The impact of Auto Mode was further assessed in a sub-group of individuals (N=51,254) with, at least, 7 days of SG data for both Auto Mode turned ON and turned OFF. The % of TIR, TBR and TAR, and the associated glucose management indicator (GMI) were evaluated for the overall OFF (2,524,570 days) and ON (6,308,806 days) periods, and across different age groups. Results: System data TIR was 71.3%; TBR was 0.4% and 1.9%, respectively; and TAR was 26.8% and 6.2%, respectively. User-wise data of Auto Mode OFF versus ON showed a mean of 70.3% of the time spent in Auto Mode, that TIR increased from 60.9% to 69.9%; and that both TBR and TAR decreased. For those 7-13yrs (N=1,417), TIR increased from 48.7% to 61.5%; TBR increased from 0.5% to 0.6% and from 2.0% to 2.2%, respectively; and TAR decreased from 49.3% to 36.3% and from 20.5% to 13.0%, respectively. For those 14-21yrs (N=4,194), TIR increased from 51.0% to 61.5%; TBR decreased from 0.7% to 0.6% and from 2.3% to 2.0%, respectively; and TAR decreased from 46.7% to 36.5% and from 18.5% to 12.5%, respectively. For those ≥22yrs (N=45,643), TIR increased from 62.2% to 70.9%; TBR decreased from 0.7% to 0.5% and from 2.6% to 1.9%, respectively; and TAR decreased from 35.2% to 27.3% and from 9.9% to 6.3%, respectively. The mean GMI decreased by 0.23% (overall), 0.48% (7-13yrs), 0.35% (14-21yrs), and 0.22% (22yrs), respectively, with Auto Mode ON versus OFF. Discussion: In over 6 million days of real-world MiniMed™ 670G system Auto Mode use in the U.S., TIR of a large pediatric and adult population with T1D improved by 9% compared to when Auto Mode was OFF, which was comparable to or exceeded the TIR observed in the smaller pivotal trials. These results further support outcomes of the pivotal trials and increased glycemic control with system use.

<![CDATA[SAT-LB122 Characterization and Functional Rescue of a Nephrogenic Diabetes Insipidus Causing S127F Substitution in V2 Vasopressin Receptor]]> Laura Szalai1, András Sziráki1, László Sándor Erdélyi1, András Balla1,2, László Hunyady1,2

1Department of Physiology, Semmelweis University, and 2MTA-SE Laboratory of Molecular Physiology, Budapest, Hungary

The concentrating function of the kidney is important to maintain the water homeostasis of the body. It is regulated by the arginine-vasopressin system through the type 2 vasopressin receptor (V2R). Loss-of-function mutations of V2R in kidney can lead to nephrogenic diabetes insipidus (NDI) which results several symptoms such as polyuria, polydipsia, and hyposthenuria.

In this study, we functionally characterized and investigated the potential rescue of a missense mutation (S127F) of the V2R. We monitored the cellular localization of the S127F mutant V2 receptor using HA-tagged receptors in confocal microscopy experiments. The S127F V2 receptor was detected only in the endoplasmic reticulum but not in the plasma membrane. Flow cytometry measurements revealed that only limited amount mutant receptor can be found on the cell surface compared to the wild type V2R. We also determined the cAMP signaling capability of the mutant receptor with BRET measurements. The S127F receptor was not able to increase the intracellular cAMP levels in response to vasopressin stimulation. Certain ER retention mutations can be rescued by pharmacological chaperones, which cause misfolded mutant receptors to present in the plasma membrane. We examined the effect of tolvaptan (a V2R antagonist) on the S127F V2 receptor. HEK293 cells were transiently transfected with the plasmid of the mutant receptor and after one day the cells were incubated for 18 hours with tolvaptan. After the pretreatment, the cells were exposed to vasopressin, and we were able to detect significant cAMP signal generation of the mutant receptor. We also checked whether the result after tolvaptan pretreatment was due to restored plasma membrane location of the receptor. We were able to demonstrate significant increase of the mutant receptors in the plasma membrane using flow cytometry. We also investigated the effect of MCF14 compound (a cell permeable high-affinity agonist for the V2R) on the mutant receptor and we found that the MCF14 was also capable to restore the cAMP signaling function of the receptor.

This work was supported by the National Research, Development and Innovation Fund (NKFI K116954 and NVKP_16-1-2016-0039).

<![CDATA[SAT-647 The Effectiveness of Insulin Pump Therapy Compared to Multiple Daily Insulin Injections in Type 1 and Type 2 Diabetes Mellitus in a Predominantly African American Population]]> Compared to multiple daily insulin injections (MDI), continuous subcutaneous insulin infusion (CSII) has proven to reach target HbA1c level with less frequent hypoglycemia, be more cost-effective, and improve quality of life. However, data on the effectiveness of CSII therapy in the African American population remain limited. The primary objective of our study was to compare the effectiveness of CSII therapy in lowering HbA1c levels in patients with type 1 diabetes (T1D) and type 2 diabetes (T2D) in a predominantly African American population. The secondary objective was to identify factors that affect the effectiveness of CSII. Participants were selected randomly from a list of patients currently receiving CSII at our institution’s diabetic clinic. Each patient’s consent was obtained over the phone or during a visit to the clinic. Primary data were collected with a questionnaire, whereas additional data, including HbA1c levels before and after starting CSII, were collected from medical records. A total of 57 participants were enrolled in the study. African Americans represented 79% of the participants; 43% of the participants were unemployed, and 56% had an annual income of less than 20,000 USD. Since commencing CSII therapy, all participants achieved a decrease in mean HbA1c level from 9.7% to 8.0% (P = 0.001), and that of African American participants decreased from 9.8% to 8.2%. Increase number of individuals at home was associated with less reduction in HbA1c levels after starting CSII therapy (P = 0.02). Overall, satisfaction with CSII therapy was high, and 63% of participants reported being very satisfied with the treatment. The mean BMI among participants while using MDI was 32.6 kg/m2 but significantly increased to 33.9 kg/m2 (P = 0.01) while using CSII. The increase in mean BMI after starting CSII therapy was significantly higher in participants with T2D than in ones with T1D (P = 0.001). While receiving MDI, female participants had a significantly higher mean BMI than their male counterparts (P = 0.02); however, that difference became nonsignificant after they began CSII therapy (P = 0.06). The level of physical activity after starting CSII therapy did not alter the risk of increased BMI. The results of our interim analysis indicate the significant effect of CSII in lowering HbA1c levels in all diabetic patients regardless of sex, race, BMI, type of diabetes, marital status, employment status, level of education, adherence to diabetic diet, physical activity, duration on CSII, and use of other antidiabetic medications. The significant increase in BMI once CSII therapy commenced may reflect the increase in insulin dose among patients who were not adherent to insulin while receiving MDI. Patients need to be aware of that side effect, and additional interventions for weight management may be considered for overweight and obese patients planning to start treatment with CSII.

<![CDATA[OR30-03 Racial Differences in Technology Use Among Type 1 Diabetes in a Safety-Net Hospital]]> Objective: There is limited data regarding the use of diabetes technology such as continuous glucose monitor (CGM) and continuous subcutaneous insulin infusion (CSII) among patients with type 1 diabetes (T1D) in a minority serving and safety-net hospital. We examined racial differences in the use of CGM and CSII in this setting.

Methods: A retrospective review of 227 patients ≥ 18 years of age with T1D seen in the Endocrinology clinic at a safety-net hospital from October 2016 and September 2017 was completed. Statistical analysis assessed the likelihood of diabetes technology use among different races.

Results: The mean age was 39, 59% male, mean duration of diabetes was 21 years, 30% overweight, 22% obesity, 80% English speaking, and 50% had government insurance. In terms of the distribution of race/ethnicity, 43% were Caucasian, 25% African American (AA), 15% Hispanic, 15% defined as other, and 2% Asian. Mean HbA1c ± standard deviation (SD) of any technology (either CGM or CSII or both) and non-technology users were 8.27 ± 1.58 and 9.49 ± 2.04, respectively. Patients who had government health insurance were found to have lower odds of using technology (odds ratio [OR], 0.43; 95% confidential interval [CI], 0.25 - 0.74) compared to patients who had private health insurance. Overall, 26% of the patients used CSII with 43% of this population Caucasian, 10.5% AA and 14.2% Hispanic. The overall CGM use was 30% with 47% of users Caucasian, 14% AA and 22% Hispanic. In a multivariable logistic regression model that adjusted for insurance and language, AA or other were found to have statistically significant lower odds of using technology (AA OR 0.25 [95% CI 0.11 - 0.53] and other OR 0.33 [95% CI 0.12 - 0.89]) compared to the Caucasian group.

Conclusion: Our study showed that the use of technology in the Caucasian group was statistically significantly higher than in the non-Caucasian groups except for the Asian group. After adjusting for insurance and language, AA and other demonstrated statistically lower rates of technology use. Racial differences in diabetes technology use were observed in our study as well as the association between technology use and lowered HbA1c. Given diabetes technology is a useful tool in reducing HbA1c and hypoglycemia, the barriers to accessing diabetes technology in non-Caucasian individuals should be addressed to decrease health disparities.

<![CDATA[OR30-07 Mixed Meal Tolerance Test (MMTT) Results from Revita-2, the First Randomized, Sham-Controlled, Double-Blind, Prospective, Multicenter Study of Duodenal Mucosal Resurfacing (DMR) Safety and Efficacy in Patients with Sub-Optimally Controlled Type 2 Diabetes (T2D)]]> <![CDATA[SAT-641 Self-Reported Psychological Stress and Glucose Variability in Type 1 Diabetes on Sensor Augmented Pump over 5 Weeks]]>


: Patients and their families and medical providers have assumed that psychologic stress impacts glucose control in T1D (Type 1 Diabetes) though studies providing confirmatory evidence in real world settings are, to our knowledge, lacking. We hypothesized that self-reported psychologic stress worsens glucose control in T1D.

: We studied 20 adults with T1D on continuous glucose monitor (CGM), sensor augmented insulin pump (SAP) prospectively at 2 clinical research centers. Patients reported psychological stress through stress diaries for 5 weeks on a severity scale of 1-7 using hard copy logs including time of onset and offset of stress and severity. For analytic purpose, grades 1-4 are classified as mild and grades 5-7 as severe.

: Baseline characteristics were age 44.9±15.0 years, F/M 12/8, HbA1c 6.8 ± 0.7%, and diabetes duration of 22.9±15.9 years. We analyzed glucose variability during days of stress versus days without stress. During a 24 hour period, patients experienced less hypoglycemia during days with stress versus days without stress (p value 0.03). During the 5 week period, patients reported 23 ± 19.5 events. We analyzed the impact of self-reported stress on CGM data streams after excluding stress events associated with missing CGM data, nocturnal events (from 12 MN to 6 AM, too few events) and events for which subjects did not provide duration of stress. Thus, we analyzed 19.5 ± 17 events per patient from 6AM to 12MN. From 6 AM to 12 MN, the episodes lasted 179 ± 255 minutes with 83 % episodes being mild/moderate and 17% moderate/ severe. Number of CGM readings during daytime stress episodes were 717± 1120 compared to 8768± 1238 during non-stress periods. Impact of stress from 6 AM to 12 MN (Mid-Night) on CGM glucose was analyzed using matched paired t test. Mean glucose (160.6±41.9 vs 148.3± 28.6) and SD (53.2 ±17.7 vs 56.1±14.6) did not show a difference; however % of time spent below 70 mg/dl was less (4 ± 5) in patients during stressful periods compared to times without stress (6.3± 5.5, P value 0.02).

: To our knowledge, this is the first study attempting to analyze the impact of self-reported stress using daily stress diaries on CGM data streams in T1D patients on SAP. The study revealed significant challenges experienced by patients in reporting adequate data. Self-reported stress was not associated with hyperglycemia. However, days of self-reported stress and periods during patients reported stress were characterized by less hypoglycemia on CGM data streams.

<![CDATA[OR30-06 Assessment of Dulaglutide Safety in Older Patient Populations in Rewind]]>


Dulaglutide (DU) was superior to placebo (PL) in reducing the incidence of Major Adverse Cardiovascular Events in the Researching Cardiovascular Events with a Weekly INcretin in Diabetes (REWIND Study) broad patient population. The safety of DU treatment is also of interest to health care providers who treat an older patient population (≥65 years of age).

The primary objective of this post-hoc analysis was to evaluate DU safety in the REWIND patient subgroup populations categorized by age (≥ 65 and < 65 years) with regards to the occurrence of the composite safety outcome of overall mortality and severe hypoglycemia. One of the key secondary objectives was first occurrence of severe hypoglycemia.

Patients were grouped into two age groups: ≥65 and <65 years. Time-to-event for the composite safety endpoint as well as individual variables were analyzed using Cox proportional hazards regression. Hazard ratios (HRs) and 95% confidence intervals (CIs) for between group treatment differences were also calculated.

Of the 9,901 patients randomized in REWIND, a total of 5,256 (DU, 2,619; PL, 2,637) were aged ≥65 years. The incidence of the composite safety outcome for patients aged ≥65 years was 399 of 2619 (15.2%) for DU-treated patients and 425 of 2,637 (16.1%) for PL-treated patients. The incidence of the composite safety outcome for those aged <65 years was 188 of 2,330 (8.1%) for DU-treated patients and 224 of 2,315 (9.7%) for PL-treated patients. Between group treatment differences (HR [95% CI]) were 0.94 (0.82, 1.08) for patients ≥65 years of age and 0.82 (0.68, 1.00) for patients <65 years of age; interaction p-value = 0.277. The incidence of the secondary outcome of first occurrence of severe hypoglycemia for patients aged ≥65 years was 46 of 2619 (1.8%) for DU-treated patients and 49 of 2,637 (1.9%) for PL-treated patients. The incidence of this outcome for patients <65 years was 18 of 2,330 (0.8%) for DU-treated patients and 25 of 2,315 (1.1%) for PL-treated patients. Between group treatment differences (HR [95% CI]) were 0.95 (0.63, 1.42) for patients ≥65 years of age and 0.71 (0.39, 1.31) for patients <65 years of age; interaction p-value = 0.443. The safety profile of DU was reviewed based upon the results of subgroup analysis of treatment emergent adverse events and serious adverse events by preferred terms for comparing PL and DU for age subgroups (≥65 years of age versus <65 years). None of the results indicated that DU has a different safety profile across the age subgroups evaluated in this post-hoc analysis.

Treatment with DU demonstrated similar safety in REWIND patients aged ≥65 years and those aged <65 years. Dulaglutide can be considered a safe and effective treatment option for use in older adults.

<![CDATA[SAT-LB121 Development of a Machine-Learning Method for Predicting New Onset of Diabetes Mellitus: A Retrospective Analysis of 509,153 Annual Specific Health Checkup Records]]> <![CDATA[OR30-04 Autonomous Drone Delivery of Insulin]]> <![CDATA[SAT-LB120 A Software Application Delivering Behavioral Therapy Improved Glycemic Control in Adults With Type 2 Diabetes]]> <![CDATA[SAT-635 Results of a Preclinical Pilot Study Evaluating 24-Hour Subcutaneous Infusion of the GLP-1 Analogue Liraglutide Delivered via the H-Patch Wearable Device]]> <![CDATA[SAT-645 An Indication for Continuous Glucose Monitoring in Glucocorticoid-Treated Patients with Diabetes Mellitus]]> <![CDATA[SAT-639 Is the Freestyle Libre Flash Glucose Monitor Accurate in the Critically Ill?]]> <![CDATA[SAT-644 A Case of the Use of the Eversense Continuous Glucose Monitor with Repeated Same-Pocket Insertion]]> <![CDATA[SAT-643 Predictors of Technology Success in Cystic Fibrosis Related Diabetes]]> <![CDATA[SAT-638 An Academic Center Experience with the Eversense Continuous Glucose Monitoring System and Assessment of Intrapatient Variability with Repeated Same-Pocket Insertion]]> <![CDATA[SAT-648 Flash Glucose Monitoring Helps Achieve Better Glycemic Control Than Conventional Self-Monitoring of Blood Glucose in Non-Insulin-Treated Type 2 Diabetes: A Randomized Controlled Trial]]>