bluuebunny/crossref_metadata_2025_split huggingface dataset:
844-P: Effects of Dapagliflozin, Metformin, or Exercise on Plasma Glucagon Concentrations in Individuals with Prediabetes: The PRE-D Trial
Authors: CLEMMENSEN KIM K.B. , BLOND MARTIN B. , AMADID HANAN , NIELSEN LEA B. , VISTISEN DORTE , KARSTOFT KRISTIAN , PERSSON FREDERIK , RIED-LARSEN MATHIAS , HOLST JENS J. , WEWER ALBRECHTSEN NICOLAI J. , TOREKOV SIGNE S. , QUIST JONAS S. , JØRGENSEN MARIT E. , FÆRCH KRISTINE 
Published: June 2020

Introduction: Glucagon plays a role in the pathogenesis of type 2 diabetes. Yet, little is known about the effect of interventions aimed at preventing progression from prediabetes to diabetes on glucagon concentrations. We examined the effects of dapagliflozin, metformin or exercise on plasma glucagon concentration in individuals with HbA1c-defined prediabetes.
Methods: 120 individuals were randomized to a 13-week intervention with dapagliflozin (10 mg once daily), metformin (850 mg twice daily), exercise (interval training 30 min 5 days/week) or control (habitual living). A 75 g oral glucose tolerance test (OGTT; 0, 30, 60 and 120 min) was administered at baseline, at 13 weeks (end of intervention) and at 26 weeks (end of follow-up). Linear mixed-effects models were used to assess effects on fasting concentration, early (0-30 min) area under the curve relative to the fasting level (rAUC0-30 min) and full rAUC0-120 min for glucagon.
Results: At baseline, the median (Q1;Q3) age was 62 (54;68) years, HbA1c 5.9% (5.7;6.1%), fasting glucagon 11 (7;15) pmol/L, and 56% were men. No differences in the glucagon measures between groups from baseline to 13 or 26 weeks were observed (Table 1).
Conclusions: 13 weeks of treatment with dapagliflozin, metformin or exercise was not associated with changes in fasting or post-OGTT glucagon concentrations in individuals with prediabetes.

Disclosure
K.K.B. Clemmensen: Research Support; Self; AstraZeneca, Novo Nordisk Foundation. Stock/Shareholder; Spouse/Partner; Novo Nordisk A/S. M.B. Blond: None. H. Amadid: None. L.B. Nielsen: None. D. Vistisen: Stock/Shareholder; Self; Novo Nordisk A/S. K. Karstoft: Employee; Spouse/Partner; Novo Nordisk A/S. F. Persson: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Novo Nordisk A/S. Research Support; Self; Amgen, AstraZeneca, Novo Nordisk A/S. Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Merck Sharp & Dohme Corp., Mundipharma International, Novo Nordisk A/S. M. Ried-Larsen: None. J.J. Holst: Advisory Panel; Self; AstraZeneca, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Zealand Pharma A/S. Other Relationship; Spouse/Partner; Antag Therapeutics. N.J. Wewer Albrechtsen: Research Support; Self; Mercodia, Novo Nordisk A/S, Novo Nordisk Foundation. Speaker’s Bureau; Self; Merck Sharp & Dohme Corp. S.S. Torekov: Research Support; Self; Novo Nordisk Inc. J.S. Quist: None. M.E. Jørgensen: Research Support; Self; Amgen, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Sanofi-Aventis. Stock/Shareholder; Self; Novo Nordisk A/S. K. Færch: None.

Funding
Novo Nordisk Foundation; AstraZeneca; University of Copenhagen; Innovation Foundation
847-P: Effect of Simple Multipronged Strategies in a Real-World Situation in Prevention of Diabetes in a Developing Country: Interim Report from South India
Authors: VISWANATHAN VIJAY , PARVEEN RIZWANA , MURUGAN PREMALATHA , KUMPATLA SATYAVANI 
Published: June 2020

Aim: Implementation of newer intervention strategies in Prediabetes (PDM) may delay the onset of DM. Aim was to see the effect of simple multi pronged approach in a real world situation to prevent progression to DM.
Methods: PDM subjects (n=220) were assigned into 2 groups(Gp) from Aug 2017 in a diabetes centre in Chennai, India. OGTT was done and diagnosis was by WHO criteria. PDM subjects; IFG, IGT, Elevated 1h PG, were assigned to control and intervention groups; (n=110 in each). Gp1 received standard care, Gp2 intensive counseling on physical activity (PA) and diet (reducing calories, carbohydrates, fat, increasing fibre). PA included walking for 150min/week and customized advice based on profession. Working subjects to avoid prolonged sitting at work place, using stairs in work areas, walking instead of hiring vehicles for short distances; Business professionals advised to walk in corridors during stay in hotels for business purpose; Housewives to do simple walks in balconies and terrace, to avoid electronic appliances. Subjects were followed up(FU) once in 6 months and repeated OGTT. Calls, mails and SMS used for FU. Those came for FU(n=91;47vs41) till Sep 2019 were included for interim analysis.
Results: Mean age of subjects was 44 years and duration of FU was 12.1 (range 4-28) months. There was a significant difference in conversion rate to DM. (Gp1 vs. 2;34 vs. 9.1%,p=0.004). The subjects who reverted back to normal was significantly higher in Gp 2 compared to Gp1 (31.8 vs. 10.6%;p =0.012). The survival curve showed mean survival time for Gp1 was 19.6 (95% CI 16.6-22.8) and Gp2 was 22.2 months (CI 19.9-24.4;p=0.014). In Gp2, there was a significant reduction in Wt, body fat (p=0.022), WHR(P=0.003), elevated 1 hr glucose (p=0.004) whereas in Gp1 reduction was observed only in glucose levels (p&lt;0.001).
Conclusion: It was possible to prevent diabetes in subjects with prediabetes using multi-pronged intervention strategies in real world scenario.

Disclosure
V. Viswanathan: None. R. Parveen: None. P. Murugan: None. S. Kumpatla: None.
850-P: Continued Monitoring of the Foreign-Body Response to Improve Long-Term Success of Macroencapsulation Devices for Type 1 Diabetes: A Proof-of-Concept Study
Authors: BEATTY RACHEL M. , DOLAN EIMEAR B. , ROBINSON SCOTT T. , OCONNOR RAYMOND , WYLIE ROBERT , DUFFY GARRY P. 
Published: June 2020

Over 463 million people are living with diabetes mellitus (DM). Type 1 DM results from an autoimmune destruction of pancreatic β-cells and accounts for 5-10% of cases. Macroencapsulation devices are a promising therapy for the treatment of T1DM as they improve insulin producing cell retention and viability. However they have had limited success due to a complex cascade of events, causing fibrous encapsulation and ultimately device failure (the foreign body response (FBR)). The FBR is a critical hurdle associated with the success of implantable devices. Here, we aim to develop a biosensing membrane that monitors the FBR in real time; ensuring steps can be taken to modulate the host’s response to the implant. A biosensing membrane was developed consisting of a conductive layer of nonmetallic, highly conductive acrylic adhesive sandwiched between two layers of 0.08 mm thermoplastic polyurethane (TPU). Thereafter the membrane was incorporated into a refillable macroencapsulation device with soft robotic capabilities. In vitro experiments were carried out using electrical impedance spectroscopy. Baseline impedance measurement were taken for porous and non-porous devices using a buffered saline (PBS). Various concentrations of Matrigel (30%, 40%, 50% and 60%) were left to solidify on the membrane, as it contains extracellular matrix proteins, making it analogous to fibrotic tissue components. A significant increase of impedance was seen across the Matrigel groups when compared to PBS alone (p&lt;0.001). A significant difference was also evident between the varying concentrations of Matrigel along the spectra, an example of which is 30% vs. 50% Matrigel at 1.2649 Hz (p&lt;0.001). We have developed a novel approach to measure the membrane permeability in vitro. Monitoring the FBR in vivo, will provide feedback on the functionality of implanted devices, to improve their longevity for the delivery of insulin producing cells.

Disclosure
R.M. Beatty: None. E.B. Dolan: None. S.T. Robinson: None. R. OConnor: None. R. Wylie: None. G.P. Duffy: None.
852-P: Is Real-Time CGM Superior to Flash Glucose Monitoring? Results of the Type 1 Diabetes CORRIDA Randomized Control Trial
Authors: SOUPAL JAN , HÁSKOVÁ ANETA , GRUNBERGER GEORGE , PETRUZELKOVA LENKA , PARKIN CHRISTOPHER , SKRHA JAN , RADOVNICKÁ LUCIE , HOROVA EVA , PRAZNY MARTIN 
Published: June 2020

Background: The trial aim was to compare the efficacy of real-time and intermittently-scanned continuous glucose monitoring (rtCGM and isCGM, respectively) in maintaining optimal glycemic control.
Methods: In this randomized study, adults with T1D and normal hypoglycemia awareness (GOLD score &lt;4) used rtCGM (Guardian Connect Mobile) or isCGM (Freestyle Libre) during 4 days of physical activity (sport period) and in the subsequent 4 weeks (home period). Main endpoints were time in hypoglycemia (&lt;3.9 mmol/l [&lt;70 mg/dl]) and time in range (3.9-10.0 mmol/l [70-180 mg/dl]). To check for bias between the different sensors used by the rtCGM and isCGM systems, the isCGM group wore an additional blinded Enlite sensor (iPro2) for 6 days.
Results: Sixty adults with T1D (age 38±13 years, HbA1c 7.8±1.1% [62±12 mmol/mol]) were randomized to rtCGM (n=30) or isCGM (n=30). All participants completed the study. Time in hypoglycemia (&lt;3.9 mmol/l) was lower among rtCGM participants in the sport period (6.8±5.5% vs. 11.4±8.6%, respectively; p=0.018) and the home period (5.3±2.5% vs. 7.3±4.4%, respectively; p=0.035). Hypoglycemia differences were significant and most notable during the night. rtCGM participants spent more time in range than isCGM participants throughout both the sport (78.5±10.2% vs. 69.7±16%, respectively; p=0.0149) and home (75.6±9.7% vs. 67.4±17.8%, respectively; p=0.0339) periods. The results were robust to the insignificant bias between rtCGM and isCGM sensors that blinded CGM found in the isCGM arm.
Conclusion: rtCGM was superior to isCGM in preventing hypoglycemia and improving time in range in T1D adults with normal hypoglycemia awareness, demonstrating the value of rtCGM alarms during exercise and in daily diabetes self-management.

Disclosure
J. Soupal: Advisory Panel; Self; Abbott. Consultant; Self; Medtronic, Roche Diabetes Care. Speaker’s Bureau; Self; Dexcom, Inc. A. Hásková: None. G. Grunberger: Research Support; Self; Medtronic. L. Petruzelkova: None. C. Parkin: Consultant; Self; Abbott, CeQur Corporation, Dexcom, Inc., DreaMed Diabetes, Novo Nordisk Inc., Onduo, Roche Diabetes Care, Valeritas, Inc. J. Skrha: Advisory Panel; Self; Boehringer Ingelheim International GmbH. Speaker’s Bureau; Self; Eli Lilly and Company, Merck Sharp & Dohme Corp. L. Radovnická: None. E. Horova: None. M. Prazny: Speaker’s Bureau; Self; Novartis Pharmaceuticals Corporation, Roche Diabetes Care. Other Relationship; Self; Abbott, Boehringer Ingelheim International GmbH, Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi, Teva Pharmaceutical Industries Ltd.

Funding
15-26705A (Program RVO-VFN00064165)
855-P: Utilization of Continuous Glucose Monitoring (CGM) and Its Impact on the Care of Veterans
Authors: BARSAMYAN GAYANE , SILVA ALEXIS DA , WHYTE LOREN , AMOLE MOROLAKE , GHAYEE HANS , LEEY JULIO A. 
Published: June 2020

Background: Continuous Glucose Monitoring (CGM) is useful in the management of patients on insulin therapy. In late 2017 a diagnostic CGM clinic was implemented in our VA hospital to aid in the management of diabetes. Our aim is to measure the impact of CGM technology in the veteran population.
Methods: Retrospective chart review of all the patients who received a diagnostic CGM (Libre Pro) between January 2018 through October 2019. Patients underwent a diagnostic CGM (blind) if they were on insulin therapy or had an A1c &gt; 7%. We evaluated: glycemic control and travel distance to the VA hospital. A total of 208 patients with diabetes were included in the study (males 91%). The most common diagnosis was T2D 86%, followed by T1D 14%. The mean A1c was 8.4% +/- 1.8. CGM data was examined by an endocrinologist and necessary medication adjustments were recommended.
Results: Patients wore the CGM on an average of 10.3 days +/- 4.6, the glucose average was 174.7 +/- 56. In 20 patients, the sensor did not recorded data. The time in range (70-180 mg/dL) was 45% +/- 26, the time above the range was 26 +/- 27. Hypoglycemia (&lt;70 mg/dL) was seen more than 5% of the CGM wore in 32% of patients. In 65% of patient, the CGM prompted a change in treatment regimen. Because CGM results and physician recommendations were communicated via telephone, patients saved an average travel distance of 60 miles SD 38.5, or roughly 120 of roundtrip travel time. Of the patients whose insulin regimen changed after CGM placement, the average HbA1c decreased from 8.2% to 7.5% (p &lt; 0.05). 100% of participants surveyed rated their satisfaction at highest and reported they would wear the CGM device again.
Conclusion: A diagnostic CGM clinic significantly improved glycemic control and reduced travel time in the studied veteran population. The diagnostic CGM was well accepted. A rational use of CGM technology may therefore lead to improved diabetes management.

Disclosure
G. Barsamyan: None. A. da Silva: None. L. Whyte: None. M. Amole: None. H. Ghayee: None. J.A. Leey: None.
857-P: Attitudes of Physicians to Chances, Risks, and Future Options Regarding Digitalization and New Technologies in Diabetes
Authors: KULZER BERNHARD , HERMANNS NORBERT , EHRMANN DOMINIC , HEINEMANN LUTZ 
Published: June 2020

There are few results on physicians’ attitudes towards digitalization and new technologies in diabetes care. In this study we asked diabetologists in Germany about their attitudes regarding digitalization and new technologies in diabetes care. A total of 326 physicians completed the survey (42,6% female; mean age: 52.2 years). Per facility, 243 people with diabetes used Flash sensor-based glucose monitoring (iscCGM), 69 used real-time continuous glucose monitoring (RCGM), and 86 used CSII. 76% of physicians reported a positive or very positive attitude towards digitalization in diabetes care and 76% of physicians rated the potential of digitalization to improve diabetes therapy as large to very large. Major advantages of digitalization referred to better communication with the patient (71.6%), decision support with therapy decisions (66.8%) and more empowerment for patients (62.3%). The three major barriers were unclear payment of digital services (80.0%), legal issues (50.3%), and high investment costs (46.5%). Currently, the most important topics are software for the analysis of glucose data (rank 1) better compatibility of different systems (rank 2) and AID-Systems (rank 3). The current and gain of importance withnin the next 5 years (in brackets) is assessed as follows: Software for the analysis of glucose data 72,3% (+15,9%); AID-Systems 22,9% (+58,6%); Apps (26,7% (+41,8%); Telemedicine 9,6% (+37,7%).
In summary, most physicians have a very positive attitude toward digitalization and perceive the potential of digitization as very high. Especially the importance of AID systems will increase. For the actual implementation in clinical routine, however, it is also important to consider the barriers described by diabetologists.

Disclosure
B. Kulzer: Research Support; Self; Abbott, AstraZeneca, Berlin-Chemie AG, Roche Diabetes Care. Speaker’s Bureau; Self; Abbott, Ascensia Diabetes Care, Berlin-Chemie AG, Eli Lilly and Company, Medtronic, Novo Nordisk Inc., Roche Diabetes Care. N. Hermanns: Advisory Panel; Self; Roche Diabetes Care. Research Support; Self; Abbott, AstraZeneca, Berlin-Chemie AG. D. Ehrmann: Speaker’s Bureau; Self; Abbott, Berlin-Chemie AG, Glooko, Inc. Other Relationship; Self; Dexcom, Inc., Roche Diabetes Care. L. Heinemann: Consultant; Self; Becton, Dickinson and Company, LifeCare, Inc., Roche Diabetes Care. Stock/Shareholder; Self; Profil Institute for Clinical Research.

Funding
Berlin-Chemie AG
858-P: Different Attitudes of Physicians, Parents of Children with Diabetes, and People with Diabetes towards Digitization in Diabetes
Authors: KULZER BERNHARD , HERMANNS NORBERT , EHRMANN DOMINIC , HEINEMANN LUTZ 
Published: June 2020

Digitalization has an increasing relevance in diabetes care. However, little is known if attitudes towards digitalization in diabetes care differ between people with diabetes (PWD) and health care professionals (HCP). As part of the annual digitalization report an online survey about the attitudes towards digitalization was completed by 3,427 PWD (PWDT1: 65,6%, ParentsT1:8.1%, PWDT2 25,5%) and 326 HCP. Attitudes towards digitalization were most positive in ParentsT1 (89,5%), PWD1T (85,3) rated it as very important) followed by HCP (75.3) and PWD2T (74.0%; p &lt;0.01). Software for analyzing glucose data was found most important by HCP (72% rated it as one of the three most important issues) whereas PWD rated its importance significantly lower (51.3% PWD1T; 47.0 PWD2T, p&lt;.01). Interestingly were Diabetes Apps only perceived as important by a minority (HCP: 23.1%; PWD1T: 30.8%; PWD2T 32.9%). Online education was found most relevant in PWD2T (32.6%), whereas PWD1T (17.8%) or HCP (13.8%) were less enthusiastic (p&lt;.01). Closed loop systems were rated as very important by 63,3% of ParentsT1, 65.6% of PWD1T, whereas only 41.7% of HCP rated them as relevant. In type 2 diabetes closed loop systems were less important (18.9%; p&lt;.0.05). Interestingly attitudes towards digitalization differ considerably between HCP and PWD. HCP perceived glucose-analysis-software as most relevant, whereas enthusiasm in PWD was less pronounced. Relevance of diabetes APPS were rated as surprisingly low in all groups. Closed loop systems are of special interest in PWD1T, whereas HCP were remarkably HCPs less enthusiastic.

Disclosure
B. Kulzer: Research Support; Self; Abbott, AstraZeneca, Berlin-Chemie AG, Roche Diabetes Care. Speaker’s Bureau; Self; Abbott, Ascensia Diabetes Care, Berlin-Chemie AG, Eli Lilly and Company, Medtronic, Novo Nordisk Inc., Roche Diabetes Care. N. Hermanns: Advisory Panel; Self; Roche Diabetes Care. Research Support; Self; Abbott, AstraZeneca, Berlin-Chemie AG. D. Ehrmann: Speaker’s Bureau; Self; Abbott, Berlin-Chemie AG, Glooko, Inc. Other Relationship; Self; Dexcom, Inc., Roche Diabetes Care. L. Heinemann: Consultant; Self; Becton, Dickinson and Company, LifeCare, Inc., Roche Diabetes Care. Stock/Shareholder; Self; Profil Institute for Clinical Research.

Funding
Berlin-Chemie AG
86-LB: Accuracy of a Factory-Calibrated, Real-Time Continuous Glucose Monitoring (CGM) System during Elective Abdominal Surgery
Authors: TRIPYLA AFRODITI , HERZIG DAVID , NAKAS CHRISTOS , DEHAIS JOACHIM , ANDREOU ANDREAS , AMIET FRANZISKA , GLOOR BEAT , VOGT ANDREAS , BALLY LIA 
Published: June 2020

Aim: To assess the accuracy of the Dexcom G6 CGM system during elective abdominal surgery.
Methods: Twenty adults with (pre-)diabetes scheduled for abdominal surgery (&gt;2h) (5 females, age 69±13 yrs, BMI 27.3±6.6 kg/m2, HbA1c 7.0±1.3%) wore the Dexcom G6 sensor from 7±2 days preoperatively until hospital discharge. Reference capillary glucose values were obtained every 20min from induction of anaesthesia until 2h post-surgery using the Accu-Chek® Inform II meter. The primary endpoint was the mean absolute relative difference (ARD) between sensor and reference method.
Results: Mean±SD operating time was 306±170min and included pancreas/liver (45%), upper GI (10%), lower GI (40%) and kidney (5%) surgeries. The mean±SD and median [interquartile range] ARD of 523 paired CGM/reference values were 12.7±8.7% and 10.4% [4.3%, 19.7%], respectively. A total of 99.4% pairs were in Clarke error grid Zones A or B (A, 76.5%, B 22.9%, Figure 1). Overall, 65.8% of sensor readings were within the limits specified by the ISO 15197:2013 norm. Sensor readings were available for 98.6% [95.9%, 100%] of the perioperative time. Intraoperative sensor replacement was required in one instance. There were no adverse device effects.
Conclusion: Perioperative use of the Dexcom G6 system showed satisfying accuracy, opening new avenues for perioperative diabetes care.

Disclosure
A. Tripyla: None. D. Herzig: None. C. Nakas: None. J. Dehais: None. A. Andreou: None. F. Amiet: None. B. Gloor: None. A. Vogt: None. L. Bally: None.

Funding
Dexcom, Inc. (HOS-2019-005)
861-P: Glycemic Improvements following Mobile-Enabled Remote Patient Monitoring: A Randomized Control Study
Authors: SHENG TONG , PARKS LINDA , BABIKIAN SARINE , SINGH VIKRAM , GREENFIELD MICHAEL , CLEMENTS MARK A. 
Published: June 2020

Mobile-enabled remote patient monitoring (RPM) may improve glycemic outcomes for people with diabetes. In the current study, we randomized persons with type 2 diabetes (PWT2D) with baseline HbA1c between 7.5% - 12.5% into either a mobile app-enabled RPM group or a control group without RPM. Participants in the RPM group were remotely monitored on a weekly basis for glycemic events and SMBG check behaviors and received telephonic coaching as needed between quarterly clinic visits. The control group received coaching only during quarterly clinic visits. In this interim analysis, we assessed HbA1c at baseline and at 3 months for both groups. Cohort characteristics for participants completing both study visits (RPM n = 80; control n = 82) included median age 60 years [IQR: 52 - 67], 48.1% female, 73.4% non-Hispanic White, 67.3% on insulin, median duration of diabetes 14 years [IQR: 9 - 20], and median baseline HbA1c of 8.4% [IQR: 7.9 - 8.9]. We observed improvements in HbA1c after 3 months in both the RPM and control groups (-0.7% [IQR: -1.0 - -.2] and -0.3% [IQR: -.8 - -.1]; both Ps &lt; .005, respectively). The improvement in HbA1c was greater in the RPM group (T = 2.47; P = .015). Among participants with baseline HbA1c ≥ 8.5%, the RPM group improved by -1.1% [IQR: -1.9 - -.4] while the control group improved by -.4% [IQR: -.8 - .3] (T = 2.63; P = .01). In the current study, we found that mobile-enabled RPM with telephonic coaching led to significantly improved HbA1c in 3 months. The HbA1c improvement was greater for PWT2D with higher baseline HbA1c, consistent with previous literature. These findings demonstrate the short-term efficacy of mobile-enabled RPM. Diabetes care teams can assist mid-life and older adults with T2D to achieve significant improvements in glycemic control via use of a mobile self-management app along with recurring short telephonic coaching sessions.

Disclosure
T. Sheng: Employee; Self; Glooko, Inc. L. Parks: Employee; Self; Glooko, Inc. S. Babikian: Employee; Self; Glooko, Inc. V. Singh: Employee; Self; Glooko, Inc. M. Greenfield: Consultant; Self; Glooko, Inc. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc.