bluuebunny/crossref_metadata_2025 huggingface dataset:
905-P: Safety, Adherence, and Treatment Satisfaction with Dapagliflozin, Metformin, or Exercise in Individuals with Prediabetes: The Pre-D Trial
Authors: AMADID HANAN , BLOND MARTIN B. , BRUHN LEA , CLEMMENSEN KIM K. , VAINØ CAMILLA T. , PERSSON FREDERIK , JØRGENSEN MARIT E. , FÆRCH KRISTINE
Published: June 2019

Introduction: Safety, adherence and treatment satisfaction to interventions with dapagliflozin, metformin or exercise in individuals with prediabetes defined by the HbA1c criterion were studied in the randomized controlled PRE-D trial.

Methods: 120 participants were randomized 1:1:1:1 to 13 weeks of either: (1) dapagliflozin (DAP, 10 mg daily); (2) metformin (MET, 1700 mg daily); (3) exercise (EXE, 5x30 min/week); or (4) control (CON, no treatment). Adverse events (AE) were collected during the entire study. Satisfaction with the interventions was assessed at 6 and 13 weeks using questionnaires. Adherence was assessed at 6 and 13 weeks by pill count in DAP and MET and each week by uploaded training data in EXE. Comparisons between groups were performed by ANOVA and chi-square tests.

Results: At baseline, the median (Q1;Q3) HbA1c was 41 (39;43) mmol/mol (5.9 (5.7;6.1)%); age 62 (54;68) years, and 44% were men. Drop outs were 2 in DAP, 1 in MET, 3 in EXE and 2 in CON. One serious AE occurred in CON (cancer). 40% of the participants experienced at least one AE during the trial (15 in DAP, 22 in MET, 7 in EXE and 4 in CON (DAP vs. MET: P=0.111; MET vs. EXE: P<0.001)). Most AEs were known side-effects of the two medications; urogenital symptoms in DAP, and gastrointestinal symptoms in MET. In all groups, median (Q1;Q3) adherence was high (DAP 100.0 (97.7;100.4)%; MET 96.0 (91.4;98.3)%; EXE 95.9 (89.9;103.2)% (session duration) and 94.5 (90.3;101.1)% (session completion)). Almost all participants in the EXE group were satisfied with their allocated intervention, while this was only true for half of the participants in the CON, DAP and MET groups. Most participants would have preferred allocation to the EXE intervention.

Conclusion: In individuals with prediabetes, treatment with DAP is safe and did not result in more side-effects than treatment with MET. Adherence to all interventions was high, although the most preferred intervention was the EXE intervention.

Disclosure

H. Amadid: None. M.B. Blond: None. L. Bruhn: None. K.K. Clemmensen: None. C.T. Vainø: None. F. Persson: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Novo Nordisk A/S. Research Support; Self; AstraZeneca, Sanofi. Speaker's Bureau; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi. M.E. Jørgensen: Research Support; Self; Amgen Inc., AstraZeneca, Danish Diabetes Association, Sanofi-Aventis. Stock/Shareholder; Self; Novo Nordisk A/S. K. Færch: Research Support; Self; Ascensia Diabetes Care, AstraZeneca, Unilever. Stock/Shareholder; Self; Novo Nordisk A/S.
906-P: Safety and Efficacy of Continuous Glucose Monitoring at an ADA Camp
Authors: SOSNOWSKI ANIELA , SCHMELTZ LOWELL
Published: June 2019

Personal use of Continuous Glucose Monitor (CGM) technology has increased as a tool to optimize glycemic control. CGM use at ADA camps has also risen over the past few years, however, some campers opt not to use their home CGM at camp as finger stick blood sugars were standard for monitoring glycemic control per camp protocols. At camp, kids are exposed to various levels of activity and different foods leading to variable insulin requirements compared to home regimens. This, in turn, could increase the number of potentially dangerous hypoglycemic episodes at camp if blood glucoses (BG) are not tightly monitored. The safety and efficacy of CGM in an ADA camp setting has not yet been published. This retrospective case-control quality review was performed on camper data from ADA Camp MIDICHA 2017 summer session. It was hypothesized that campers using CGM would have fewer severe glucose excursions (hypo- or hyperglycemia) compared to campers not using CGM. Of the 483 campers, 36 campers used CGM at camp. Another 36 campers (age/gender matched) who did not use CGM were identified as controls. Campers were further matched based on insulin administration method (pump vs. injections) and cabin (to reduce activity variability). BG data including average BG levels, hypoglycemic events (BG <50, <70, <80 mg/dL), and hyperglycemic events (BG >250, >300, and >400 mg/dL) were collected and analyzed. There was no significant difference in average BG levels of campers using CGM and those not using CGM (BG=179.6 vs. 178.53 mg/dL; p=0.8832). Binary logistics regressions were used to generate odds ratios for glycemic event data. Campers using CGM had a 74% lower relative risk of having at least one severe hypoglycemic episode (BG <50) during their week at camp compared to controls (14% vs. 38%; OR: 0.26 (95% CI: 0.09, 0.73); P = 0.0012). No significant differences were noted in hyperglycemia frequency amongst campers using CGM and controls. Therefore, the use of CGM should be encouraged in the camp setting to reduce the incidence of severe hypoglycemia.

Disclosure

A. Sosnowski: None. L. Schmeltz: Advisory Panel; Self; Sanofi. Speaker's Bureau; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc.
91-OR: Stem Cell-Derived Tissue-Associated Regulatory T Cells Suppress the Activity of Pathogenic Immune Cells in Autoimmune Diabetes
Authors: SONG JIANXUN J.
Published: June 2019

The auto antigen (Ag)-specific regulatory T cells (Tregs) from pluripotent stem cells (PSCs), i.e., PSC-Tregs, have the ability to suppress autoimmunity. PSC-Tregs can be programmed to be tissue-associated and to infiltrate into local inflamed tissues to suppress autoimmune responses after adoptive transfer. Nevertheless, the mechanisms by which the auto Ag-specific PSC-Tregs suppress the autoimmune response remain to be fully elucidated. In this study, we generated the functional auto Ag-specific Tregs from the induced PSC (iPSCs), i.e., iPSC-Tregs, and investigated the underlying mechanisms of autoimmunity suppression by these Tregs in a type 1 diabetes (T1D) murine model. A double transgenic (Tg) mouse model of T1D was established in F1 mice in which the first generation of RIP-mOVA Tg mice that were crossed with OT-I T cell receptor (TCR) Tg mice was challenged with vaccinia viruses expressing OVA (VACV-OVA). We show that adoptive transfer of OVA-specific iPSC-Tregs greatly suppressed autoimmunity in the animal model and prevented the insulin-secreting pancreatic β cells from destruction. Further, we demonstrate that the adoptive transfer significantly reduced the expression of ICAM-1 in the diabetic pancreas and inhibited the migration of pathogenic CD8+ T cells and the production of the pro-inflammatory IFN-γ in the pancreas. These results indicate that the stem cell-derived tissue-associated Tregs can robustly accumulate in the diabetic pancreas, and through down-regulating the expression of ICAM-1 in the local inflamed tissues and inhibiting the production of pro-inflammatory cytokine IFN-γ, suppress the migration and activity of the pathogenic immune cells that cause T1D.

Disclosure

J.J. Song: None.

Funding

American Diabetes Association (1-16-IBS-281); National Institutes of Health (R01AI121180, R21AI109239)
910-P: Improved Accuracy of 14-Day Factory-Calibrated FreeStyle Libre System with New Glucose Algorithm
Authors: KARINKA SHRIDHARA ALVA , BAILEY TIMOTHY S. , BRAZG RONALD L. , BUDIMAN ERWIN S. , CASTORINO KRISTIN , CHRISTIANSEN MARK P. , LILJENQUIST DAVID R. , LIU HANQING
Published: June 2019

We evaluated the accuracy of FreeStyle Libre 14-day system with new glucose algorithm in adults ages 18 years or older. This was a non-randomized, multi-center, single-arm study that enrolled 144 subjects with types 1 and 2 diabetes on intensive insulin therapy at 5 U.S. sites. Subjects participated in 3 clinic sessions of up to 10 hours each over the 14- day wear period to obtain comparator data. Glucose concentrations were manipulated to obtain glucose concentrations across the system dynamic range of 40-500mg/dL. System performance was evaluated in terms of percent accuracy with respect to venous plasma reference using YSI2300. Accuracy was characterized as the proportion of sensor results that were within ±15%, ± 20% of their paired YSI values for glucose levels ≥ 70 mg/dL or within ± 15 mg/dL, ± 20 mg/dL of YSI for glucose levels < 70 mg/dL. Other accuracy metrics such as mean bias and mean absolute relative difference (MARD) of the FreeStyle Libre System with respect to YSI were also evaluated and are presented in Table 1.

FreeStyle Libre System with new algorithm showed 92.8% of the results within ±20mg/dL/±20% of the venous plasma reference with an overall MARD of 9.2% based on the data from 144 subjects with evaluable sensors.

Disclosure

S.A. Karinka: Employee; Self; Abbott. T.S. Bailey: Advisory Panel; Self; Abbott. Consultant; Self; Capillary Biomedical, Inc., Eli Lilly and Company, Medtronic, Novo Nordisk Inc., Sanofi. Research Support; Self; Abbott, Ascensia Diabetes Care, Becton, Dickinson and Company, Boehringer Ingelheim Pharmaceuticals, Inc., Calibra Medical, Capillary Biomedical, Inc., Companion Medical, Dance Biopharm Holdings Inc., Dexcom, Inc., Diasome Pharmaceuticals, Inc., Eli Lilly and Company, GlySens Incorporated, Kowa Pharmaceutical Europe Co. Ltd., Lexicon Pharmaceuticals, Inc., Medtronic, Novo Nordisk Inc., POPS! Diabetes Care, POPS! Diabetes Care, Sanofi, Senseonics, vTv Therapeutics, Xeris Pharmaceuticals, Inc., Zealand Pharma A/S. Speaker's Bureau; Self; Abbott, MannKind Corporation, Medtronic, Novo Nordisk Inc., Sanofi US, Senseonics. R.L. Brazg: Research Support; Self; Abbott, Ascensia Diabetes Care, Eli Lilly and Company, Gan & Lee Pharmaceuticals, Janssen Pharmaceuticals, Inc., Medtronic, Novo Nordisk Inc., ProSciento, Roche Diagnostic USA, Xeris Pharmaceuticals, Inc. E.S. Budiman: Employee; Self; Abbott. Stock/Shareholder; Self; Abbott, AbbVie Inc. K. Castorino: Consultant; Self; Abbott. Research Support; Self; Abbott, Dexcom, Inc., Drawbridge, Novo Nordisk Inc., Sanofi US. M.P. Christiansen: Research Support; Self; Abbott, Biolinq, Dexcom, Inc., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Medtronic MiniMed, Inc., Novo Nordisk A/S, Sanofi-Aventis, Xeris Pharmaceuticals, Inc. D.R. Liljenquist: None. H. Liu: Employee; Self; Abbott Laboratories.

Funding

Abbott Diabetes Care
912-P: T2D Users of a Digital Diabetes Management System Experience an Increase of In-Range Glucose Levels Linked to App Engagement
Authors: HERSHCOVITZ YIFAT , DAR SHARON , FENIGER EITAN
Published: June 2019

Time in-range blood glucose level (70-140 mg/dl) is one of the greatest challenges when managing diabetes. Digital engagement can play a pivotal role in the care of patients with diabetes and other chronical conditions, potentially improving patient’s compliance. In this study DarioTM Blood Glucose Monitoring System (BGMS) users were evaluated to examine whether higher App engagement contributes to better diabetes management.

Method: A retrospective data evaluation study was performed on the DarioTM cloud database. A population of active type 2 diabetic (T2D) users (>15 measurements per month on average) was evaluated. The study assessed the ratio of in-range blood glucose readings (70-140 mg/dL) as a function of App engagement level for 6 months as recorded in the database compared to first 30 days as a starting point of analysis.

Results: A population of 4917 T2D non-insulin users measuring more than 15 times per month on average during 6 months in a row was evaluated. The ratio of in-range (70-140 mg/dL) readings was increased following 3 months in correlation to the level of tagging meal reference/carbs/physical activity occurrences (4.0%, 9.1% and 11.9% for tagging 0-1, 1-2 and >2 times per day on average, respectively) and sustained for 6 months (3.1%, 7.0% and 12.2%, respectively). In subgroup analysis focusing on users entering their meal reference, high correlation was observed following 3 months with an increase of in-range measurements in 4.6%, 8.4% and 12.0% for 0-1, 1-2 and >2 meal reference tagging per day on average, respectively, and maintained stability over 6 months period (3.2%, 7.4%, and 12.5%, respectively).

Conclusion: The combination of a glucose meter, an App and engaged user may promote better behavioral modification which enhance and sustain adherence to diabetes management.

Disclosure

Y. Hershcovitz: Employee; Self; DarioHealth. S. Dar: Employee; Self; DarioHealth. E. Feniger: Employee; Self; DarioHealth.
913-P: Reduction of Blood Glucose Average Less than 140mg/dl in People with Type 2 Diabetes Using a Digital Diabetes Management System
Authors: HERSHCOVITZ YIFAT , DAR SHARON , FENIGER EITAN
Published: June 2019

Patients with high average of blood glucose levels (>180mg/dL) are at high risk for developing clinical complications. Digital engagement can play a pivotal role in the care of patients with diabetes, assisting to reduce their blood glucose average (BG avg) over time. The study reviews DarioTM user’s ability to sustain BG avg<140 mg/dL over time while using the DarioTM Blood Glucose Monitoring System (BGMS).

Method: A retrospective data evaluation study was performed on the DarioTM cloud database. A population of active T2D users that continuously measured for 6 months was evaluated. The study assessed their BG avg and estimated A1C (eA1C) values based on blood glucose readings as recorded in the database. Values were calculated in periods of 3 and 6 months and compared to their first 30 days as a starting point analysis.

Results: A group of 1248 Dario BGMS T2D active users (1.98 measurements per day on average during 6 months in a row) with BG avg >140mg/dL (eA1C>6.5) was evaluated. All (100%) reduced their BG avg along 6 months on average. A group of 31% (387) achieved BG avg of <140 mg/dL (eA1C<6.5) following 3 months showing 19% reduction on average from baseline (132.38±13.36 vs.162.79±25.41 mg/dL and eA1C 6.24±0.46 vs. 7.30±0.88) and sustained their glycemic control during a 6 months period (131.57±13.86 mg/dL and eA1C 6.21±0.48). Subgroup analyses of 568 non-insulin users revealed that 40% (226) achieved a BG avg <140 mg/dL following 3 months (131.95±13.21 vs.161.67±24.18 mg/dL and eA1C 6.22±0.46 vs. 7.26±0.84) and sustained for 6 months period (131.03±13.70 mg/dL and eA1C 6.19±0.47). Along the 6 months period, hypo events (<50mg/dL) per user per month on average remained stable.

Conclusion: Patients using a digital diabetes management platform have the potential to promote behavioral modification and sustain adherence to diabetes management, demonstrating better glycemic control.

Disclosure

Y. Hershcovitz: Employee; Self; DarioHealth. S. Dar: Employee; Self; DarioHealth. E. Feniger: Employee; Self; DarioHealth.
917-P: Prediction Abilities for a Risk of Hypoglycemia in Glycemic Variability Metrics
Authors: TAKEISHI SOICHI , TSUBOI HIROKI
Published: June 2019

We investigated prediction abilities for a risk of hypoglycemia (R-hypo) in intermediate-term glycemic variability metrics (GVM) using continuous glucose monitoring (CGM) data. We cross-sectionally investigated CGM (FreeStyle Libre Pro) data of 104 patients with type 2 diabetes whose 24 h glucose levels were measured continuously for 13 days during hospitalization. Values for 13 days in all GVM were evaluated. We have proposed GVM as follows: mean of daily difference 1 (MODD1) ÷ mean glucose level (MGL) × 100 (MODD1/M), mean absolute glucose (MAG) ÷ MGL × 100 (MAG/M), and glycemic variability percentage (GVP) ÷ MGL × 100 (GVP/M). The standard deviation (SD), MODD1, MAG, GVP, and MGL were significantly negatively correlated to the low blood glucose index (LBGI) (r=-0.34∼-0.68, p<0.001). The coefficient of variation (CV), MODD1/M, MAG/M and GVP/M were significantly positively correlated to the LBGI (r=+0.22∼+0.53, p=0.03∼<0.001). The prediction ability for LBGI>2.5 in MAG/M was significantly higher than that in CV and GVP/M. The prediction ability for LBGI>5.0 in MAG/M was significantly higher than that in CV, MODD1/M and GVP/M. The prediction ability for LBGI>2.5 was higher than that for LBGI>5.0 in MODD1/M (Table). The GVM which are divided by MGL should be used to predict a R-hypo. Among them, MAG/M has the highest prediction ability for a R-hypo. In addition, MODD1/M reflects a midium R-hypo more strongly than a high R-hypo.

Disclosure

S. Takeishi: None. H. Tsuboi: None.
919-P: Canada Real-World Analysis of Flash Glucose Monitoring and Impact on Time-in-Range and Hypoglycemia
Authors: BERARD LORI , VIRDI NAUNIHAL , DUNN TIMOTHY
Published: June 2019

Prior analyses of global data from real world use of flash glucose monitoring have associated frequency of scanning with greater time in range and lower mean glucose, glucose variability, and hypoglycemia. These analyses did not include data from North America as flash glucose monitoring became available in late 2017. The objective of this follow-up analysis is to focus on real world outcomes in patients using flash glucose monitoring in Canada. A server collected anonymized data from patients whose flash glucose readers are uploaded (by patient or health care professional). Data from patients within Canada was analyzed through September 2018 (approximately 1 year). To understand the relationship between time in range and glucose variability with monitoring frequency, individuals were divided into 10 equal sized groups based on scanning frequency. Average ± SE time in range (glucose 70 mg/dL - 180 mg/dL [3.9 mmol/L - 10.0 mmol/L]) and time in glucose ≤ 54 mg/dL (∼3.0 mmol/L) was calculated for each group.

This analysis includes 15,424 readers, 95,103 sensors, and 108 million glucose measurements with an average of 12 scans per day. Patients in the lowest scanning frequency decile (3.7 scans per day) spent 12.6 ± 0.14 hours in range and 28.8 ± 1.2 minutes with a glucose ≤ 54 mg/dL. Patients in the highest scanning frequency decile (29.2 scans per day) spent 16.6 ± 0.12 hours in range and 22.1 ± 1.0 minutes with a glucose ≤ 54 mg/dL.

Real-world data from Canada demonstrates that higher frequency of scanning is associated with increased time in range and decreased hypoglycemia. This analysis is consistent with prior analyses and suggests that patients with diabetes who scan to obtain their glucose more frequently derive greater benefit than patients who scan less frequently.

Disclosure

L. Berard: Advisory Panel; Self; Eli Lilly and Company. Consultant; Self; Abbott, Ascensia Diabetes Care, AstraZeneca, Bayer AG, Becton, Dickinson and Company, Janssen Pharmaceuticals, Inc., LifeScan Canada, Mylan, Novo Nordisk Inc., Sanofi. Research Support; Self; Montmed Inc. Speaker's Bureau; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Merck & Co., Inc. N. Virdi: Employee; Self; Abbott, Proteus Digital Health. Stock/Shareholder; Self; Johnson & Johnson. T. Dunn: Employee; Self; Abbott Laboratories.
924-P: Pattern Mining of Trajectories of Glucose Values of Continuous Glucose Monitoring System by Artificial Intelligence in Type 2 Diabetes Patients
Authors: MAKINO MASAKI , YOSHIMOTO RYO , KONDO-ANDO MIZUHO , YOSHINO YASUMASA , HIRATSUKA IZUMI , MAKI WAKAKO , SEKIGUCHI-UEDA SAHOKO , KAKITA AYAKO , SHIBATA MEGUMI , SEINO YUSUKE , TAKAYANAGI TAKESHI , ONO MASAKI , KOSEKI AKIRA , KUDO MICHIHARU , HAIDA KYOICHI , YANAGIYA RYOSUKE , HAYAKAWA NOBUKI , SUZUKI ATSUSHI
Published: June 2019

Background: Thanks to technological advancements for medical devices, we can measure glucose by the minute for weeks using a sensor called the continuous glucose monitoring (CGM) system. CGM is time-series data and has been available since devices with low measurement error appeared 10 years ago. CGM can be relied upon to help make treatment decisions. A major issue regarding CGM is clinical interpretation by physicians.

Methods: CGM data were obtained by flush glucose monitoring system in 156 type 2 diabetes patients. We divided the patients into 2 groups by mean HbA1c levels during 6 months before getting CGM data. High HbA1c group (High) had their mean HbA1c levels with equal to or above 7% and below 9%. Low HbA1c group (Low) had their mean HbA1c levels with equal to or above 5% and below 7%. The patients with their HbA1c levels equal to or above 9% was excluded from this study. We conducted the experiment with manually created dataset that is designed for evaluating the performance of trajectory extraction. Artificial intelligence (AI) performed pattern mining of raw data of flush glucose in 4 to 8 sequential data sets.

Results: Among 156 patients, there were 83 High group patients, while 58 patients were defined as Low group. We excluded 15 patients from this study due to high HbA1c levels, while there was no patient with HbA1c level below 5%. AI constructed 1292 patterns of glucose trajectories from CGM data. We found that 67 patterns were significantly different between High and Low groups 8p<0.05).

Conclusion: In this session, we propose a method of extracting the trajectories of glucose values of CGM in type 2 diabetes patients. Our method could contribute to better CGM interpretation.

Disclosure

M. Makino: None. R. Yoshimoto: None. M. Kondo-Ando: None. Y. Yoshino: None. I. Hiratsuka: None. W. Maki: None. S. Sekiguchi-Ueda: None. A. Kakita: None. M. Shibata: None. Y. Seino: None. T. Takayanagi: None. M. Ono: None. A. Koseki: Employee; Self; IBM. M. Kudo: Employee; Self; IBM. K. Haida: None. R. Yanagiya: None. N. Hayakawa: None. A. Suzuki: Research Support; Self; Chugai Pharmaceutical Co., Ltd., Dai-ichi Life Insurance Company, IBM, MSD, Ono Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited. Speaker's Bureau; Self; Asahi Kasei Corporation, Daiichi Sankyo Company, Limited, Eli Lilly and Company, Mitsubishi Tanabe Pharma Corporation, Taisho Pharmaceutical Co., Ltd.
926-P: Frequency of Hypoglycemia by Gender in Healthy Individuals without Diabetes—Not So Different After All
Authors: DUBOSE STEPHANIE , LI ZOEY , SHAH VIRAL N. , SHERR JENNIFER , TAMBORLANE WILLIAM V. , BERGENSTAL RICHARD M. , BECK ROY
Published: June 2019

It is a common belief that females experience more hypoglycemia than males. We aimed to assess whether this was true in a cohort of healthy individuals without diabetes. In this multicenter study, healthy, nondiabetic children and adults (age 7 to 80 years, normal BMI, HbA1c <5.7%, and no positive islet antibodies) were included. Each participant wore a blinded Dexcom G6 for ∼10 days. Overall, 153 participants were analyzed, 66% female, 84% non-Hispanic white, with a mean HbA1c of 5.1±0.3%. Race/ethnicity, BMI, and HbA1c were similar between females (n=101) and males (n=52) but females were older (mean age 34±21 years vs. 25±20 years). Mean average glucose, time in range (TIR) 70-140 mg/dL and coefficient of variation (CV) were similar between females and males (100 vs. 99 mg/dL; 95% TIR and 17% CV for both). There were no statistically significant differences in the percentage of participants with at least one hypoglycemic event (15 consecutive minutes <54 mg/dL), the number of events, or the duration of events between females and males. Additionally, the percentages of time spent below various cutoffs of hypoglycemia were similar between females and males (Table). At least when it comes to frequency, severity and duration of hypoglycemia, these data support the contention that men and women are not so different after all.

Disclosure

S. DuBose: None. Z. Li: None. V.N. Shah: Advisory Panel; Self; Sanofi US. Consultant; Self; Dexcom, Inc. J. Sherr: Advisory Panel; Self; Bigfoot Biomedical, Eli Lilly and Company, Insulet Corporation. Consultant; Self; Lexicon Pharmaceuticals, Inc., Medtronic, Sanofi. Research Support; Self; Dexcom, Inc., Insulet Corporation. W.V. Tamborlane: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic MiniMed, Inc., Novo Nordisk Inc., Sanofi, Takeda Pharmaceutical Company Limited. R.M. Bergenstal: Research Support; Self; Abbott, Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Glooko, Inc., Hygieia, JDRF, Johnson & Johnson Medical Devices Companies, Lilly Diabetes, Medtronic MiniMed, Inc., Merck & Co., Inc., National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk Inc., Roche Diabetes Care, Sanofi. Other Relationship; Self; Abbott, Helmsley Charitable Trust, Hygieia, Lilly Diabetes, Novo Nordisk Inc., Onduo LLC, Sanofi, UnitedHealth Group Inc. R. Beck: Other Relationship; Self; Abbott Laboratories, Ascensia Diabetes Care, Bigfoot Biomedical, Dexcom, Inc., Insulet Corporation, Lilly Diabetes, Roche Diabetes Care, Tandem Diabetes Care.

Funding

The Leona M. and Harry B. Helmsley Charitable Trust
93-LB: Defining High Glycemic Variability in Type 1 Diabetes: Comparison of Multiple Indexes to Identify Patients at Risk of Hypoglycemia
Authors: GOMEZ ANA MARIA , HENAO DIANA , IMITOLA ANGELICA M. , TABOADA LUCIA B. , ROBLEDO MARÍA ALEJANDRA , CRUZ KERLY V. , RONDÓN MARTÍN , MUNOZ OSCAR M. , JARAMILLO MAIRA GARCIA , LEÓN-VARGAS FABIAN
Published: June 2019

Background: International consensus on the use of continuous glucose monitoring (CGM) recommends coefficient of variation (CV) as the metric of choice to express glycemic variability (GV) with a cutoff of 36% to define unstable diabetes. Even though, CV is associated with hypoglycemia in T2DM patients, the evidence on the use of one particular measure of GV in T1DM patients as a predictor of hypoglycemia is limited.

Methods: A cohort of T1DM ambulatory patients was evaluated using CGM. Number and incidence rate of events < 54 and < 70 mg/dL were calculated. Bivariate and multivariate analysis of different glycemic indexes and clinical variables were performed to identify those associated with hypoglycemia. ROC curve analysis for each of the glycemic indexes were performed to define the best indexes and optimal cutoff thresholds to discriminate patients at risk of hypoglycemia.

Results: Seventy-three patients were included. A total of 128 events of hypoglycemia < 54 mg/dL were recorded in 34 patients and 350 events < 70 mg/dL were registered in 51 patients. CV was the only variable significantly associated with hypoglycemia <54 mg/dL in the multivariate analysis (aRR: 1.44, 95% CI: 1.10-1.88; p=0.008). %CV, HbA1c and mean glucose were associated with events <70 mg/dL. Analysis of ROC curves showed that, among GV metrics, CV had the best performance to discriminate patients with events < 54 mg/dL (AUC: 0.87, 95% CI: 0.79-0.95) and events <70 mg/dL (AUC: 0.79, 95% CI: 0.68-0.90) with optimal cutoff thresholds values of 34% and 31% respectively. Among glycemic risk indexes, LBGI showed the best performance.

Conclusions: This analysis shows that CV is the the best GV index, and LBGI the best glycemic risk index, to identify patients at risk of clinically significant hypoglycemia and hypoglycemia alert events in T1DM patients.

Disclosure

A. Gomez: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Novo Nordisk Inc. Speaker’s Bureau; Self; Abbott, AstraZeneca, Medtronic MiniMed, Inc., Merck Sharp & Dohme Corp. D. Henao: Research Support; Self; Novo Nordisk Inc. Speaker’s Bureau; Self; Abbott, Medtronic MiniMed, Inc., Novo Nordisk Inc. A.M. Imitola: None. L.B. Taboada: Other Relationship; Self; Medtronic MiniMed, Inc. M. Robledo: None. K.V. Cruz: None. M. Rondón: None. O.M. Munoz: None. M. Garcia Jaramillo: None. F. León-Vargas: None.
93-OR: Expression of Genes Associated with Host Viral Responses in Insulitic Islets
Authors: NELSON GRACE , LENCHIK NATALIYA I. , CAMPBELL-THOMPSON MARTHA , MATHEWS CLAYTON E. , ATKINSON MARK A. , GERLING IVAN C.
Published: June 2019

Background: Type 1 diabetes (T1D) is an autoimmune condition long hypothesized to be enhanced or triggered by viral infections. Islet pathology in T1D is characterized by destruction and subsequent loss of insulin producing pancreatic beta cells. In this study we evaluated specific physiological pathways associated with the disorder’s pathogenesis by examining differential expression of host genes within islets, with special emphasis on identifying evidence suggestive of responses to a viral infection.

Methods: Pancreatic tissue samples were obtained from nondiabetic donors, autoantibody positive donors, and donors with T1D through the Network for Pancreatic Organ donors with Diabetes (nPOD) program. Laser microdissection was used to isolate individual islets based on immunohistochemical documentation of presence or absence of insulin (INS+) and T-lymphocytes (CD3+). RNA was isolated and microarray used to assess transcriptomes.

Results: Increased expression in insulitic islets was seen in a variety of genes including TLR pathway inhibitors: LAIR1 (p=2.77E-8), SIGLEC7 (p=8.33E-6), SIGLEC9 (p=1.59E-4), and C1q (p=6.11E-16); as well as genes downstream of the TLR pathway: CD163 (p=2.34E-11), CTSS (p=9.89E-7) and IFNγ (p=5.9E-8). We also observed variances in the frequency of viral response genes, including those known to be associated with T1D at a GWAS level: STAT1 (p=1.64E-7) and OAS3 (p=1.04E-8). Pathway analysis demonstrated increased representation of antigen processing and presentation genes (p=3.0E-14).

Conclusions: In sum, these studies noted an increased expression of multiple host genes associated with anti-viral responses. The demonstration of specific protein involvement in host responses to infections are up-regulated in T1D islets, specifically in those with signs of immune activation and residual insulin positivity (INS+CD3+), supports the potential involvement of host viral response pathways in the development of T1D.

Disclosure

G. Nelson: None. N.I. Lenchik: None. M. Campbell-Thompson: None. M.A. Atkinson: None. I.C. Gerling: None.

Funding

National Institutes of Health (UC4DK104155)
931-P: Hyperglycemia following Early vs. Late Meal Boluses
Authors: NORLANDER LISA M. , NYKAZA EDWARD T. , ARBITER BRANDON , BUCKINGHAM BRUCE A. , LAL RAYHAN
Published: June 2019

Background: Late-meal blousing increases the risk of hyperglycemia following a meal using meter blood glucose data. We used data from the Donors to the Tidepool Big Data Donation project to evaluate real-life bolusing and glycemic excursions using continuous glucose monitoring (CGM).

Methods: We analyzed 214,087 meal boluses from 120 subjects, mean age 22 years (range 3-66), average CGM glucose 155mg/dL (range 103-239 mg/dL). We screened for meals meeting the following criteria: carbs of ≥20g, premeal glucose between 70 and 200mg/dL, no glucose <70mg/dL in the hour prior to bolus, no other insulin bolus 2 hours prior and 4 hours post bolus, 95% of CGM readings available 30 min before and 4 hour after the meal bolus. A pre-meal bolus was defined by the glucose rising <0.4mg/dL/min and a late meal boluses by a glucose rise of ≥1mg/dL/min in the 20 minutes prior to the bolus.

Results: The post meal bolus had significantly higher CGM glucose levels at the time of bolus (p<0.001), and a significantly higher post prandial peak glucose (p<0.001). There was no statistical difference in the average amount of meal carbs for the two groups.

Conclusions: Using integrated pump and CGM data from a large data base, late meal bolusing occurred with 12% of selected meal boluses and was associated with higher CGM values at the time of bolus, and higher peak post prandial CGM values.

Disclosure

L.M. Norlander: None. E.T. Nykaza: None. B. Arbiter: Employee; Self; Tidepool Project. Stock/Shareholder; Self; Abbott Laboratories, Dexcom, Inc. B.A. Buckingham: Advisory Panel; Self; ConvaTec Inc., Novo Nordisk Inc., Profusa, Inc. Consultant; Self; Medtronic MiniMed, Inc. Research Support; Self; Beta Bionics, ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Medtronic MiniMed, Inc., Tandem Diabetes Care. Other Relationship; Self; Insulet Corporation, Tandem Diabetes Care. R. Lal: Consultant; Self; Abbott.
940-P: Toward a Novel Tiny Continuous Glucose Monitor (CGM): Step 3, Building an Enhanced Ratiometric Detector Using a Buried Dual Junction Diode (BDJ)
Authors: PENNATHUR SUMITA
Published: June 2019

Continuous glucose monitors (CGM) improve overall glucose control in patients with diabetes, but have many disadvantages for users. We are developing a novel CGM that will be painless to insert and wear, and easy to use. Our goal is an on-body, wirelessly transmitting CGM device that extracts interstitial fluid with a dime-sized patch of microneedles and optically detects glucose from extremely small fluid volumes. Our first step in 2016 was developing an innovative method for fabricating silicon microneedles, sharp and small enough to penetrate skin without pain (160 µm in height, 50 µm in diameter, and 100 µm in width). The next year we not only demonstrated that our microneedle fabrication is repeatable and reliable but also developed an entirely new, stable, non-enzymatic glucose-sensing method based on a variation of the well-known fluorescent-based boronic acid sensor. Since then, we have further refined our sensor for conductivity sensing as well as demonstrated a novel integrated silicon buried dual-junction (BDJ) photodiode-based system with which to detect glucose with our sensor.

Specifically, we built a buried dual junction (BDJ) photodiode for color or ratiometric optical sensing. We employed a simple polycarbonate UV filter to block stray 380 nm pump light from reaching the BDJ detector, and the signal-to-background ratio is further enhanced by incorporating a yellow band filter between the LED and sample reservoir. To test the optical performance of our photosensor, cationic polymer PFP and fluorescein/coumarin are excited at 380 nm and measured at the peak 425 and 525 nm. These data prove that our device is capable of detecting ∼µM of fluorophores in a ∼7 µL of volume. Next, we demonstrated the diffusion of molecules via hollow silicon microneedles. These data show that integration if all components can enable for the first time, a compact, wearable optical biosensor.

Disclosure

S. Pennathur: Board Member; Self; Laxmi Therapeutic Devices. Consultant; Self; Alveo Technologies.

Funding

American Diabetes Association/Pathway to Stop Diabetes (1-17-VSN-18)
947-P: Glucose Variability: Insulin Pump vs. Multiple Daily Injection in Type 1 Diabetes Evaluated by Continuous Glucose Monitoring
Authors: PROIETTI ADRIAN E. , DAGHERO ANDREA V. , SCAPUZZI MARIA LUJAN , IGLESIAS MARIA L. , NOGUEIRA JUAN PATRICIO
Published: June 2019

Background: This study compared the effects of insulin pump (IP) vs. multiple daily injection therapy (MDI) on glucose variability (GV) evaluated by professional Continuous Glucose Monitoring (CGM) in real-life condition of patients with type 1 diabetes (T1D).

Methods: This was a parallel study in 120 T1D patients, with a mean age of 31.2 years, and hemoglobin A1c (HbA1c) level of 8.3%; 99 patients were treated MDI (66 patients with Glargine U100, 13 with Detemir, 20 with Degludec U100) and 21 with IP. The primary end point was the coefficient of variation (CV) of blood glucose. Secondary end points included HbA1c, mean amplitude of glycemic excursions (MAGE). We compared IP vs. MDI.

Results: IP was not different to MDI respect to CV (37.94±1.68 vs. 40.32±075; p=0.12) and HbA1c (8.59±0.20 vs. 8.23±0.15; p=0.39). There were also no significant differences between treatment groups with respect to MAGE (308.75±11.28 vs. 309.22±5.17; p=0.92) and total doses of insulin (35.14±4.77 vs. 42.88±2,22; p=0.36). In multiple regression analyses, the CV was positively associated with MAGE and negatively with HbA1c only 22% (R2: 0,22, p = 0.01).

Conclusion: In our population in real world data, no significant difference was observed in the GV indicators in patients treated with MDI vs. IP.

Disclosure

A.E. Proietti: None. A.V. Daghero: None. M.L. Scapuzzi: None. M.L. Iglesias: None. J.P. Nogueira: None.
948-P: Analysis of Key Metrics for Continuous Glucose Monitoring Data in Adults without Diabetes
Authors: FRECKMANN GUIDO , PLEUS STEFAN , ULBRICH SINA , WALDENMAIER DELIA , BAUMSTARK ANNETTE , LINK MANUELA , HAUG CORNELIA
Published: June 2019

Continuous glucose monitoring (CGM) may help to improve diabetes therapy. An international consensus paper calls for standards in the CGM data analysis, suggesting reports of standardized “key metrics.” Data from people without diabetes might be useful in the interpretation of the suggested key metrics.

In this study, 39 adults without diabetes used two iscCGM systems (FreeStyle Libre) in parallel for the 14-day sensor life. Based on collected iscCGM data the key metrics mean glucose, standard deviation, glycemic variability (CV), time in various glucose ranges, low blood glucose index (LBGI) and high blood glucose index (HBGI) were calculated. Instead of estimated A1C, its recently proposed replacement, the glucose management index (GMI), was calculated.

Out of 78 sensors, 73 sensors achieved a data capture rate of at least 80%, the minimum required by the consensus statement. Direct comparison of the two sensors worn in parallel was possible for 35 subjects. Specific results are provided in the Table.

In this study, some key metrics calculated from iscCGM data of adults without diabetes showed considerable variation between subjects as well as within subjects. Although the reliability of these key metrics depends on how accurately CGM data depict “true” glucose levels, these findings may be helpful in the interpretation of these key metrics in diabetes patients.

Disclosure

G. Freckmann: Advisory Panel; Self; Abbott, Novo Nordisk Pharma GmbH, Sensile Medical AG. Consultant; Self; Metronom Health Europe, Roche Diabetes Care, Sanofi. Speaker's Bureau; Self; Ascensia Diabetes Care, Dexcom, Inc., Ypsomed AG. S. Pleus: None. S. Ulbrich: None. D. Waldenmaier: None. A. Baumstark: None. M. Link: None. C. Haug: None.
951-P: A Clinical Decision Support System for Basal-Bolus Insulin Therapy Is Moving to Hospital Routine Care: Efficacy, Safety, and Usability Confirmed
Authors: LICHTENEGGER KATHARINA M. , ABERER FELIX , TUCA ALEXANDRU C. , DONSA KLAUS , HÖLL BERNHARD , SCHAUPP LUKAS , PLANK JOHANNES , BECK PETER , FRUHWALD FRIEDRICH , PIEBER THOMAS R. , MADER JULIA K.
Published: June 2019

Glycemic goals of inpatients should consider the prevention of hypoglycemia and hyperglycemia. To achieve glycemic control in the hospital, guidelines recommend basal-bolus insulin therapy as the preferred method for treatment of persistent hyperglycemia and the use of clinical decision support systems. The aim of this study was to investigate the efficacy, safety and usability of the GlucoTab® decision support system in hospital routine care of non-critically ill patients with type 2 diabetes at 3 general wards of a university medical center. In total, 150 patients with type 2 diabetes requiring insulin therapy (56 female, age 68 ± 11 years, HbA1c 77 ± 24 mmol/mol, diabetes duration 15 ± 11 years, BMI 30 ± 6 kg/m2, length of hospital stay 9 ± 7 days) were treated with basal-bolus insulin therapy guided by GlucoTab®, a mobile decision support system providing automated workflow tasks and suggestions for insulin dosing to nurses and physicians. In total 4879 capillary blood glucose values (BG) were documented. By using the GlucoTab® system in routine care, a mean daily BG of 159 ± 32 mg/dl could be achieved. 68.8% of all BG measurements were in the acceptable range (70-<180 mg/dl). The percentage of BG values <40, <70 and ≥300 mg/dl was 0.02%, 2.2% and 2.3%, respectively. Health care professional’s adherence to the suggested insulin doses (n=7407) and workflow tasks (n=9927) was high (>93% and 91%, respectively). The study confirmed that the GlucoTab® system supports a safe, efficacious and user-friendly glycemic management of basal-bolus insulin therapy in hospital routine care. Further research will include decision support for alternative therapy types for patients with less stringent goals to prevent hypoglycemia in hospital routine care.

Disclosure

K.M. Lichtenegger: None. F. Aberer: None. A.C. Tuca: None. K. Donsa: Stock/Shareholder; Self; decide Clinical Software GmbH. B. Höll: Employee; Self; decide Clinical Software GmbH. L. Schaupp: None. J. Plank: None. P. Beck: Employee; Self; decide Clinical Software GmbH. Stock/Shareholder; Self; decide Clinical Software GmbH. F. Fruhwald: None. T.R. Pieber: Advisory Panel; Self; ADOCIA, Arecor Limited, AstraZeneca, Novo Nordisk A/S, Sanofi. Speaker's Bureau; Self; Novo Nordisk A/S. J.K. Mader: Advisory Panel; Self; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Prediktor Medical, Roche Diabetes Care, Sanofi. Speaker's Bureau; Self; Abbott, AstraZeneca, Dexcom, Inc., Novo Nordisk Inc. Stock/Shareholder; Self; decide Clinical Software GmbH.

Funding

Research Studio Austria "GlucoTab“ (844737)
955-P: Evaluating Early Initiation of Continuous Glucose Monitoring (CGM) among Youth Diagnosed with Type 1 Diabetes (T1D)
Authors: WONG JESSIE J. , CLAY SUSAN MICHELLE , FORLENZA GREGORY P. , HANES SARAH , WADWA R. PAUL , HOOD KOREY K.
Published: June 2019

Background: CGM has glycemic and psychosocial benefits. The current study sought to evaluate the specific impact of introducing CGM soon after diagnosis of T1D.

Methods: A sample of 55 youth recently diagnosed with T1D were randomized to either an intervention (start Dexcom G5 CGM within 1 month of diagnosis) or control (monthly blinded CGM use) condition. HbA1c data and youth and parent surveys were collected at baseline, 13 and 26 weeks and glucose levels were measured via blinded or unblinded CGM. T-tests were used to compare groups based on glycemic and psychosocial outcomes.

Results: Participant ages ranged from 3-18 (mean 11.1 ± 3.6years). On average, youth in the intervention group spent less time in hypoglycemia at 13 (2.0% vs. 7.5%, p=0.002) and 26 (1.9% vs. 4.9%, p=0.056) weeks and had elevated scores on the Diabetes Self Management Profile at week 13 (54.5 vs. 43.9 p=0.021) and more openness with glucose monitoring at weeks 13 (15.0 vs. 12.2, p=0.057) and 26 (16.42 vs. 12.12, p=0.014). Conversely, intervention group youth reported worse quality of life (QoL) at 13 weeks (8.5 vs. 3.0, p=0.020). At 26 weeks, the intervention group had slightly higher diabetes distress among youth (32.2 vs. 25.4, p=0.089) but lower diabetes distress among parents (42.5 vs. 50.9, p=0.050). Adding to prior reports from this study, these results show reduced hypoglycemia, improved attitudes toward diabetes technology, and satisfaction with glucose monitoring as well as decreased parent reported youth QoL.

Conclusion: Introducing CGM soon after diagnosis appears to have substantial benefits, including prevention of hypoglycemia and bolstering youth self-management practices and openness to glucose monitoring. Given that early CGM initiation may negatively impact QoL in youth with T1D, as indicated by both youth and parent report, youth with T1D may benefit from education around use of CGM as well as psychosocial support for those with lower QoL.

Disclosure

J.J. Wong: None. S. Clay: None. G.P. Forlenza: Advisory Panel; Self; Dexcom, Inc. Consultant; Self; Medtronic MiniMed, Inc., Tandem Diabetes Care. Research Support; Self; Dexcom, Inc., Insulet Corporation, Medtronic MiniMed, Inc., Tandem Diabetes Care. S. Hanes: None. R. Wadwa: Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Beta Bionics, MannKind Corporation, Novo Nordisk Inc., Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Other Relationship; Self; Dexcom, Inc. K.K. Hood: Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc. Speaker's Bureau; Self; Johnson & Johnson Diabetes Institute.

Funding

Dexcom, Inc.
958-P: Analysis of Accuracy of Flash Glucose Monitoring in Japanese Subjects without Diabetes
Authors: YOSHINO SATOSHI , YAMADA EIJIRO , OKADA SHUICHI , SAITO TSUGUMICHI , YAMADA MASANOBU
Published: June 2019

Background: The Abbott FreeStyle Libre Flash glucose monitoring system (FGM) is one of the recently introduced personal continuous glucose monitoring systems. While FGM use has spread widely, few reports have examined the factors that determine its accuracy.

Methods: We performed oral glucose tolerance tests (OGTTs) in 25 Japanese subjects without diabetes.

Results: The mean absolute relative difference (MARD) in sensor glucose values of FGM (FGM-SG) with reference to self-monitoring of blood glucose (SMBG) levels was 10.9±4.1% during OGTTs. However, surprisingly, the MARD in FGM-SG with reference to plasma glucose (PG) levels was 20.3±10.3% during OGTTs, demonstrating the problems in accuracy of FGM-SG compared with that of PG levels in our study setting; however, this issue was not observed on comparison with accuracy of SMBG levels. Parkes error grid analyses showed that FGM-SG with either SMBG or PG levels as reference met the criteria of the International Organization for Standardization. Moreover, the MARD showed negative correlations with fasting blood sugar, homeostasis model assessment insulin resistance index, area under curve (AUC)-PG, and body mass index (BMI); multiple regression analyses revealed that BMI was the most contributing factor to the MARD between FGM-SG and PG levels. In other words, lean people showed more MARDs.

Conclusions: Inaccurate FGM data could potentially increase the risk of inappropriate treatment. Consideration of such factors might be critical while using FGM.

Disclosure

S. Yoshino: None. E. Yamada: None. S. Okada: None. T. Saito: None. M. Yamada: None.
972-P: Expanded Real-World Use Reaffirms Strong Correlation between Scanning Frequency of Flash Glucose Monitoring and Glucose Control
Authors: LANG JORDAN , JANGAM SUJIT , DUNN TIMOTHY , HAYTER GARY
Published: June 2019

Previous analysis of real-world flash glucose monitoring data showed a strong correlation between higher scan frequency and improved glucose control. Robust adoption since then has resulted in a ten-fold increase in data available to confirm these findings. Deidentified data between September 2014 to September 2018, comprising of 592,328 readers and 5,842,193 sensors worldwide, were analyzed. Scan rate per reader was determined and each reader was sorted into twenty equally-sized rank-ordered groups, categorized by scan frequency. Glucose parameters were calculated for each group, including estimated HbA1c, time above, below and within range identified as 70 -180 mg/dL. Users performed an average of 12 scans per day (median:10, interquartile range: 7-14) with a total of 1.71 billion hours of readings and 896 million scans. The estimated HbA1c decreased from 8.2% to 6.7% (66.2 to 50.0 mmol/mol) as scan rate increased from lowest to highest scan groups (3.5 and 38.5 scans/day, respectively; p < 0.001). The time below 54 mg/dL decreased by 26% (p < 0.001), from 31.6 to 23.4 min/day. Time above 240 mg/dL decreased from 5.9 to 2.2 h/day (63%, p < 0.001). Time in range increased from 11.7 h/day to 16.8 h/day (43%, p < 0.001). Overall, 63.0% of readers had time in range >12 hours/day, while comparing those below and above 10 scans per day were 52.1% and 73.1%, respectively. The expanded real-world analysis confirms prior observations that higher rates of scanning to self-monitor glucose strongly correlate with improved glucose measures, including decreased mean glucose and time in hyper- and hypoglycaemia as well as increased time in range.

Disclosure

J. Lang: Employee; Self; Abbott. S. Jangam: Employee; Self; Abbott Laboratories. Employee; Spouse/Partner; Abbott Laboratories. T. Dunn: Employee; Self; Abbott Laboratories. G. Hayter: Employee; Self; Abbott.