bluuebunny/crossref_metadata_2025 huggingface dataset:
881-P: Patient and Clinician Usability Rating of Commercially Available Diabetes Apps
Authors: FU HELEN N. , ADAM TERRENCE , WYMAN JEAN F.
Published: June 2019

Background: Despite the significant clinical benefit in hemoglobin A1c reduction, diabetes applications (apps) use is low. The goal of this study was to assess app usability based on the heuristics of intuitive designs and the Theory of Self-Determination on motivation. Diabetes apps were evaluated by clinicians and patients for motivational properties that target patient needs to learn blood glucose trends and patterns (competence), to personalize choices to change (autonomy) and to connect with healthcare providers (feel related).

Method: Two top-rated Android apps, mySugr and OnTrack, underwent evaluation by four clinicians (three physicians and one diabetes nurse educator) and 92 adults with type 1 or 2 diabetes on insulin. Each app was tested for seven diabetes-related tasks including data entry, analysis, report view options (by the day of the week and by meals), and report email. Clinicians evaluated if the app violated Nielsen's Ten Heuristics on user-friendly designs. Both clinicians and patients completed a questionnaire on the ease of use - System Usability Scale (SUS).

Results: OnTrack and mySugr target two behavior needs important for motivation in diabetes self-management. App usability was suboptimal to low. Clinicians rated an “F” for mySugr (15 ± 5) scoring < 60 and a “D” for OnTrack (61 ± 28) scoring between 60-69. Patients rated an “F” for mySugr (55 ±18) and a “D” for OnTrack (68 ±15). Clinicians identified many heuristic violations (n=169): mySugr (90) and OnTrack (77). Heuristic principles violated the most frequently were “Help and Documentation” (38), followed by “Aesthetic and Minimalist Design” (43) and “Error Prevention” (24).

Conclusions: Commercially available diabetes app usability was marginally acceptable to unacceptable according to clinicians and patients. Future diabetes app design needs to target patient motivation and incorporate key heuristic principles to provide self-tutorials, enhance graphical or screen views, and eliminate error-prone conditions.

Disclosure

H.N. Fu: None. T. Adam: Stock/Shareholder; Self; GlaxoSmithKline plc. J.F. Wyman: None.

Funding

Robert Wood Johnson Foundation; Sigma Theta Tau International-Zeta Chapter
888-P: High Engagement over 12 Months with a Text Messaging Intervention for Type 2 Diabetes
Authors: NELSON LYNDSAY A. , GREEVY ROBERT A. , SPIEKER ANDREW J. , WALLSTON KENNETH , MAYBERRY LINDSAY S.
Published: June 2019

The extent to which users engage with an intervention can reveal why and when it is effective. However, studies reporting on engagement with text messaging interventions are often short-term and frequency/dose varies widely. Further, limited research has examined engagement in diabetes text-based interventions with racially and socioeconomically diverse patients. We examined engagement with a 12-month text messaging intervention for T2D as part of a larger RCT. We recruited adults with T2D from community health centers and primary care clinics. At baseline, patients completed a survey and an A1c test. Patients randomized to intervention received daily one-way and interactive texts for the first 6 months, and then had the option to receive fewer texts (i.e., low-dose) for the remaining 6 months. We examined associations between patient characteristics and low-dose choice, and the association between choice and subsequent engagement, as measured by odds of response to interactive texts. The sample (N=250) had a mean age of 55.9±9.8 years, was 55% female, and 52% non-white; 41% had ≤ a high school degree, 41% had incomes <$25K, and mean A1c was 8.6±1.8%. On average, patients responded to 81% (IQR: 75-96%) of texts over 12 months. At 6 months, 44% chose to continue receiving daily texts, whereas 56% chose low-dose for the remaining 6 months. No patient characteristics were associated with choice, and choice was not associated with later engagement (b=-0.20, p=0.52). Odds of responding was lower over time in both groups (p<.001) but did not differ across groups. Average response rate was 73% (61-97%) in months 8-12. Among diverse patients with T2D, engagement with text messages remained high over 12 months. Almost half the sample elected to continue receiving daily texts for the entire 12 months. Engagement was similar in both groups in the final 6 months. Our findings support using texts to engage diverse patients in mobile health interventions. Providing an option for text frequency may help sustain engagement.

Disclosure

L.A. Nelson: None. R.A. Greevy: None. A.J. Spieker: None. K. Wallston: None. L.S. Mayberry: None.

Funding

National Institute of Diabetes and Digestive and Kidney Diseases (R01DK100694); Center for Diabetes Translational Research (P30DK92986)
89-LB: Low Level of Agreement in Evaluation of Continuous Glucose Monitoring Profiles
Authors: HACHMANN-NIELSEN ELISE , KVIST KAJSA , BERGENSTAL RICHARD M.
Published: June 2019

Nearly 20 years after the introduction of Continuous Glucose Monitoring (CGM) it is clear that CGM reports can effectively guide clinical decision making but the large amount of information contained in the CGM profile may also complicate the evaluation of the profiles.

We hypothesized that there would be a relatively low level of agreement in evaluations of CGM-profiles performed by a diverse group of HCPs and patients, with higher level of agreement between diabetes experts. We investigated the interobserver variation in the two groups when asked to subjectively prioritize 9 Ambulatory Glucose Profiles from ‘best’ to ‘worst’ through an internet-based portal (www.glucoseprofile.com).

A total of 88 responses were collected. The groups were defined as non-experts (N = 73) and self-evaluated experts (>10 years of experience and > 1 CGM evaluation per week) (N = 15). The interobserver variation between the groups were calculated using Interclass Correlation Coefficients (ICC) showing moderate agreement among the non-experts group (ICC = 0.60) and a good degree of agreement among the diabetes experts (ICC=0.75) [Figure].

Despite standardized CGM metrics and reports there is still a relatively low level of agreement in interpretation and ranking of CGM-profiles even among diabetes experts. This may allow for undesirable variation in diabetes management. More tools are needed to optimize the use of CGM for clinical decision making.

Disclosure

E. Hachmann-Nielsen: Employee; Self; Novo Nordisk A/S. K. Kvist: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. 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.

Funding

Novo Nordisk
896-P: Standard Breakfast as a More Physiological Test of Glucose Tolerance than OGTT in Obese Patients—Relationship with 24-h Glycemic Variability and Insulin Resistance
Authors: REZKI AMEL , FYSEKIDIS MARINOS , COSSON EMMANUEL , VALENSI PAUL
Published: June 2019

Introduction: Standardized meals have been suggested as a potential tool to diagnose glycemic disorders. In obese patients with different glycemic status (normal glucose tolerance (NGT) or intermediate hyperglycemia (IH: IFG and/or IGT), or type 2 diabetes (T2D), we aimed to examine the relationship between the glucose response to 75g OGTT and a “French” standard breakfast test including 75g carbohydrates, and between the glucose response to both tests with insulin resistance (IR) and glucose variability (GV) derived from continuous glucose monitoring (CGM).

Patients and Methods: We included 80 obese patients without known dysglycemia, separated according to OGTT in 24 NGTs, 37 with IH and 19 T2Ds. Several IR indexes were calculated. GV was evaluated by calculating SD-glucose, CONGA, J index and MAGE from 24h CGM including the OGTT on day 1 and the standard breakfast test on day 2.

Results: Using CGM data, the 120-minute area under curve (AUC-glucose) and glucose peak were similar after OGTT and breakfast, the time to the interstitial glucose peak was longer (p<0.02) and glucose 2 hours levels higher after OGTT than after breakfast (p<0.001). AUC-glucose after OGTT and breakfast correlated with 24h GV indexes respectively on days 1 and 2 (p<0.001), with IR indexes including HOMA-IR and FIRI, and negatively with QUICKI, ISI0min, ISI120min, Gutt and Matsuda indexes (p<0.02 to 0.0001). Most of 24h GV indexes correlated with several IR indexes (p<0.01 to 0.0001). All T2Ds, 35 out of the 37 patients with IH and 3 of the 24 NGTs had either glucose 60 or 120 minutes after breakfast ≥7.8 mmol/l, giving a 96% positive predictive value to predict abnormal results during OGTT.

Conclusion: In obese patients without known dysglycemia, the magnitude of glucose excursion after the standard breakfast correlates with the glucose response to OGTT, 24h GV and IR. This more physiological test offers a good performance to predict abnormal response to OGTT.

Disclosure

A. Rezki: None. M. Fysekidis: None. E. Cosson: Board Member; Self; Abbott, Boehringer Ingelheim Pharmaceuticals, Inc., LifeScan, Inc., Lilly Diabetes, Medtronic, Merck Sharp & Dohme Corp., Novartis France, Novo Nordisk A/S, Roche Diagnostics France, Sanofi. Research Support; Self; Air Liquide, Lilly Diabetes, Novo Nordisk A/S, Roche Diagnostics France, Roche Foundation, Sanofi. P. Valensi: None.
899-P: Barriers and Facilitators to Implementation of the Group-Based Diabetes Prevention Program within a Large Health-Care System
Authors: HALLEY MEGHAN , NASRALLAH CATHERINE , SZWERINSKI NINA K. , PETERSEN JOHN P. , ROMANELLI ROBERT J. , AZAR KRISTEN
Published: June 2019

Background: Lifestyle change programs based on the National Diabetes Prevention Program (DPP) are associated with clinically significant weight loss and decreases in cardiometabolic risk factors. However, these benefits depend on successful implementation in the real world. Studies have examined implementation in community settings, but less is known about integration in healthcare systems. We examined the barriers and facilitators to successful DPP implementation in a large multi-site healthcare system.

Methods: Using purposive sampling, we conducted semi-structured interviews with past and present DPP lifestyle coaches in the healthcare system. Qualitative data were analyzed using deductive and inductive techniques to identify themes, which were organized into a conceptual framework using an iterative consensus process.

Results: We conducted 33 interviews across 19 clinics serving 10 counties. Six interrelated themes and subthemes were identified: 1) Broader context, including the surrounding physical and demographic context; 2) Institutional setting, including finances, infrastructure, and personnel; 3) Program characteristics, including curriculum, administration, cost, goals, and visibility; 4) Participant recruitment, including screening and referrals; 5) Lifestyle coaches’ role, including their characteristics, behaviors, and morale; and 6) Participants’ characteristics, including attention, retention and group dynamics. Each theme and subtheme describes a key factor that served as a barrier and/or facilitator in the implementation process depending on the site-specific context and on each lifestyle coach’s approach to implementation.

Conclusion: The study highlights the complexities associated with implementing a group-based DPP in a large healthcare system and emphasizes the key role of lifestyle coaches in overcoming site-specific barriers to implementation and adapting the program to the local context.

Disclosure

M. Halley: None. C. Nasrallah: None. N.K. Szwerinski: None. J.P. Petersen: None. R.J. Romanelli: None. K. Azar: None.

Funding

National Institute of Diabetes and Digestive and Kidney Diseases
90-LB: Treatments and Trends in Glycemic Control for Patients with Type 2 Diabetes Mellitus Treatment following Metformin
Authors: BAJPAI SANJAY K. , NAIR RADHIKA , CHANGAMIRE TICH , SHEER RICHARD , WANG QIANQIAN , PATEL HIREN
Published: June 2019

Objectives: To compare characteristics of patients with type 2 diabetes mellitus (T2DM) who did/did not achieve short-term (3-12 months) and/or long-term (24-36 months) glycemic control (HbA1c < 7%) following treatment with metformin (MET) monotherapy and determine factors associated with glycemic control.

Methods: This was a retrospective study using administrative claims from a national health plan. Patients with T2DM on MET monotherapy for ≥1 year, HbA1c ≥7% and initiating a new glucose lowering medication between January 2010-December 2014 were eligible for this study with date of the first prescription set as index date. Patients were required to have ≥1 HbA1c between 24-36 months post-index; a subset of patients with HbA1c values between 3-12 months was identified. Descriptive analyses were used to compare patients by achievement of glycemic control. Multivariable analyses were used to evaluate factors associated with glycemic control.

Results: A total of 7,416 patients were eligible of which 6,561 had data for short-term assessment. Mean A1c (%) during pre-index was 8.2+/-1.3 which decreased to 7.3+/- 1.1 in the short-term and 7.5+/-1.3 in long term, with 39.8% achieving long-term glycemic control. Among patients achieving glycemic control in the short-term (44.4%), 58.2% maintained glycemic control in long-term. For every 1.0% higher HbA1c at baseline, odds of attaining glycemic control decreased by 16% in the short-term and 12% in the long-term. Treatments associated with greater odds ratio (OR) (95% CI) of achieving HbA1c <7% in long-term were SGLT2 inhibitors [OR= 2.45 (1.93-3.12)], GLP-1 RA [OR= 1.95 (1.60-2.36)] and Thiazolidinediones [OR= 1.73 (1.52-1.96)].

Conclusion: Although the majority of patients intensifying treatment after MET monotherapy did not achieve optimal glycemic goals in the long term, understanding how to facilitate treatments that encourage better glycemic control can aid in treatment selection.

Disclosure

S.K. Bajpai: Employee; Self; Eli Lilly and Company. R. Nair: Stock/Shareholder; Self; Abbott Laboratories, AbbVie Inc., GlaxoSmithKline plc. T. Changamire: Employee; Self; Humana. R. Sheer: None. Q. Wang: None. H. Patel: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company.
916-P: Comparison of Personal vs. Professional Continuous Glucose Monitoring Related to Calibration
Authors: TAKEISHI SOICHI , TSUBOI HIROKI
Published: June 2019

Sensor glucose levels (SGs) after the capillary blood glucose level used to calibrate (BGc) are not influenced by the future BGc in personal continuous glucose monitoring (CGM) (psCGM), but by the future BGc in professional CGM (pfCGM). This leads to a stronger influence of the BGc on SG just after the BGc (SGja) in psCGM than that in pfCGM. Inaccurate SGs or BGc causes dissociation. We investigated the accuracy of psCGM and pfCGM. We cross-sectionally investigated 192 sets including the BGc and SGs before and after the BGc in 12 patients (pts) with type 2 diabetes (96 sets in 6 pts with psCGM (GUARDIAN CONNECT) and pfCGM each). Preprandial and bedtime glucose levels on days 2-5 (of wearing CGM devices) were evaluated in all pts. We calculated the degree of dissociation between the BGc and nearest SGs (DBS) using the following formula: |[(SG just before BGc (SGjb) - BGc) + (SGja - BGc)]/2| = calibration index (CaI) A. We calculated the effect of the BGc on post-SGs (EBpS) using the following formulae: |(SGja - SGjb) - [(SGjb - SG 15 min before SGjb)/3]| = a, |(SGjb - BGc)|/|(SGja - BGc) (if 0, 1)| = b, √(a × b) = CaI B. CaI C was defined as CaI A + CaI B. We defined cases wherein the BGc had dissociation cause potential (CBGc) as both I and II in psCGM group (psCGMg) (I: CaI B > mean in each of two consecutive sets, II: magnitude relation between SGjb and BGc was different between two consecutive sets). CaI B was significantly higher in psCGMg than in pfCGM group (pfCGMg) (p < 0.001), but CaI A and CaI C were not different. CaI A and CaI B were significantly correlated in psCGMg (r = 0.54, p < 0.001), but did not correlate in pfCGMg. High CaI B was significantly associated with high CBGc (p = 0.002). The optimal cutoff value for CBGc in CaI B was 5.5 (sensitivity: 88%, specificity: 85%, area under the curve: 0.87 p < 0.001) in psCGMg. The EBpS was stronger in psCGMg than in pfCGMg, while the DBS was not different. The result of CaI C shows equal accuracy of psCGM to pfCGM in terms of retrospective data. At CaI B > 5.5, recalibration is necessary.

Disclosure

S. Takeishi: None. H. Tsuboi: None.
94-LB: Measurement Accuracy of a Newly Developed Prototype System for Noninvasive Glucose Monitoring
Authors: FRECKMANN GUIDO , PLEUS STEFAN , WINTERGERST PETER , WALDENMAIER DELIA , JENDRIKE NINA R. , LINK MANUELA , HAUG CORNELIA
Published: June 2019

Non-invasive glucose monitoring (NIGM) may be beneficial for people with diabetes in avoiding the need for finger pricking to obtain blood samples. The aim was to assess measurement accuracy of a prototype system for NIGM in a mixed outpatient and in-clinic setting.

A total of 10 subjects with type 1 diabetes participated in the study which lasted for 27 days per subject. Subjects wore a FreeStyle Libre (FL) system on the upper arm for continuous glucose monitoring, and they performed standard blood glucose (BG) monitoring with a Contour® next ONE meter and NIGM at the thenar with the prototype system. At least 6 times per day, measurement sets were performed which consisted of 1st BG meter measurement, 1st FL scan, 1st NIGM measurement, 2nd BG meter measurement, 2nd FL scan, 2nd NIGM measurement. Results from the same measurement set were averaged by device before analysis. In-clinic sessions took place on the 6th day and on the 27th day, during which rapid glucose excursions with high and low glucose values were induced. During these sessions, measurement sets were performed every 15 minutes between 0730 and 1500. Data from 24/19 days were used for calibration of the NIGM system for the first/second group of five subjects. The data from the remaining 3/8 days (including 1 in-clinic day each) were used for independent validation of the calibration. For data from validation days, mean absolute relative differences (MARD) were calculated for FL and the NIGM prototype in comparison with the BG meter values for each subject.

Outpatient MARD ranged from 8.0% to 26.2% for FL (mean: 16.1%) and from 9.1% to 26.9% for the prototype (mean: 20.2%), whereas in-clinic MARD ranged from 13.7% to 45.3% for FL (mean 23.3%) and from 19.8% to 36.0% for the prototype (mean: 25.5%).

Although MARD was somewhat higher for the NIGM prototype than for FL, this proof-of-concept study showed promising results for the newly developed NIGM prototype.

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. P. Wintergerst: None. D. Waldenmaier: None. N.R. Jendrike: None. M. Link: None. C. Haug: None.

Funding

RSP Systems A/S
946-P: HbA1c Is Already Not Enough for Glucose Control Assessment in Type 1 and Type 2 Diabetes
Authors: TOTOMIROVA TZVETELINA , DASKALOVA IVONA , ARNAUDOVA MILA
Published: June 2019

Recommendations for control assessment in diabetes include measurement of glycated hemoglobin (HbA1c) and pre- and postprandial blood glucose (BG). Continuous Glucose Monitoring is a novel method that could be used for insulin dose adjustment but also reveal precise 24-hour glycemic profile through followed days. In our study we compare routine used glycemic control assessment tools with results derived from CGM record. We studied a group of 106 patients (60 men, 46 women; mean age 58.23±10.81 years), 21 patients with type 1 diabetes (on intensified regimen) and 85 patients with type 2 diabetes (33 - on oral medications, 31 - on pre-mixed insulin, 21 - on intensified insulin regimen). Continuous glucose monitoring with iProTM was performed for seven days and HbA1c was measured at the end of this period. In oral medication treated and pre-mixed insulin treated group a high positive correlation was found between HbA1c (7.13±0.85% and 7.64±1.40) and average blood glucose level during period of CGM (7.15±1.80mmol/l and 7.64±1.48mmol/l) (p<0.01). Correlation coefficient were (r1=0.742 and r2=0.642) (p<0.05). In group of type 2 patients on intensified regimen correlation was lower (r3=0.507). No correlation was found in type 1 diabetes group (r4=0.083). Only in oral treated type 2 group HbA1c showed correlation with the number of above and below excursion. No correlations were found between HbA1c and number of excursions in other groups. We conclude that HbA1c is appropriate assessment tool only in patients with stable glucose control as in type 2 on oral treatment. In all other cases we need additional method like pre-and postprandial glucose measurement. CGM could be also useful for assessment nevertheless short period of time that it reflects.

Disclosure

T. Totomirova: None. I. Daskalova: None. M. Arnaudova: None.
949-P: Associations between Body Composition and Glycemic Variability in Japanese Type 2 Diabetes
Authors: MIZOGUCHI ASAKO
Published: June 2019

Objective: This clinical study investigated the association between body composition and glycemic variability in Japanese type 2 diabetes patients.

Research Design and Methods: A total 34 Japanese type 2 diabetes out-patients were enrolled. Glycemic variability were assessed using continuous glucose monitoring (CGM) and we excluded the glycemic data within 24 hours after mounted. Their body composition were measured by Inbody 770 (Inbody Japan, Japan). Statistical analyses were done by SPSS ver.23 and the data were showed by mean±SD.

Results: Baseline characteristics are indicated thus; male/female 22/12, 68.4±10.2 years old, period of CGM measuring 11.1±2.8 days, height 1,61±0.08 m, body weight 64.3±13.3 kg, A1c6.9±0.8%, duration of diabetes 5.4±9.8 years, maximum glucose 256±56 mg/dl, minimum58±17 mg/dl, average 137±31 mg/dl, SD37.8±14.9 mg/dl, body muscle weight 42.2±8.6 kg, body fat weight19.2±7.3kg. The patients with hypoglycemia (less than 50 mg/dl) in CGM were significantly less body height (1.64±0.07 m vs. 1.57±0.08 m, P=0.012), less body weight (70±14.6 kg vs. 57.8±7.9 kg, P=0.005)and less amount of total body muscle (45.8±8.9 kg vs. 38.3±6.4 kg, P=0.009).The patients with glycemic SD more than 40 mg/dl were lighter weight(67.7±13.9 kg vs. 57.4±9.0 kg, P=0.033) and lower BMI (25.6±3.9 vs. 22.2±2.3, P=0.014). There were no significantly difference in body fat and muscle amount in each group. The patients who had more than 5% of duration time with more than 200 mg/dl were lower BMI (25.6±4.0 vs. 22.7±2.6, P=0.035)and less amount of total body muscle (44.6±9.3 vs. 37.9±5.8, P=0.018).In multiple regression analysis after adjustment by age, sex and BMI hyperglycemia was found be significantly associated with body muscle percentage and body fat percentage.(OR0.007 CI;0.000to0.791 P=0.040 OR0.010 CI;0.000to0.852 P=0.042, respectively).

Conclusions: The Japanese type 2 patients with less body muscle amount may tend to show higher glycemic variability.

Disclosure

A. Mizoguchi: None.
952-P: Improving Glycemic Management of Patients with Type 2 Diabetes in Acute Geriatric Care by Using a Basal Insulin Algorithm Incorporated into a Workflow and Decision Support System
Authors: LIBISELLER ANGELA , LICHTENEGGER KATHARINA M. , KOPANZ JULIA , CAMPO ANTONELLA DE , WIESINGER TATJANA , WEIDINGER LISA , LAUBREITER LISA , SCHIPPINGER WALTER , TRUSKALLER THOMAS , LACKNER BETTINA C. , BECK PETER , HÖLL BERNHARD , PIEBER THOMAS R.
Published: June 2019

Introduction: Glycemic management can be complex in elderly patients despite of multimorbidity and polypharmacy. International guidelines recommend individualizing therapy goals by using easy to handle basal insulin regimen with low hypoglycemia risk. An algorithm for basal insulin therapy (including basal-plus) based on the individual’s health status was developed and incorporated into GlucoTab®. This tablet-based workflow and decision support system provides suggestions for insulin therapy, blood glucose (BG) management and offers workflow support by visualization of open tasks. The study objective was to investigate efficacy and safety of the GlucoTab® system in patients at an acute geriatric hospital.

Methods: A randomized controlled trial was performed in 58 patients with type 2 diabetes aged ≥ 65 years. Patients in the intervention group (IG) (n=31) were treated according to GlucoTab® basal insulin algorithm, patients in the control group (CG) (n=27) received diabetes therapy according to physician’s prescription electronically documented in GlucoTab®.

Results: Patients of both groups (sex: IG=65% vs. CG=63% females, age: 78±6 vs. 76±6 years, BMI: 29±6 vs. 30±6 kg/m2 and HbA1c: 56±10 vs. 61±13 mmol/mol) were attributed an individual health status: 74% moderate and 26% good health. The percentage of fasting BG (FBG) values within health-dependent FBG target range was 59±33% vs. 51±31%. BG values below, within, and above health dependent target range occurred in 4%, 75% and 22% vs. 1%, 75% and 25%, respectively. In both groups no severe hypoglycemic event <40 mg/dl was observed. The number of BG values in the ranges 40 to 69 mg/dl was 3 vs. 2, respectively. Mean FBG was 142±49 vs. 150±38 mg/dl and mean daily BG was 195±74 vs. 201±46 mg/dl.

Conclusion: The GlucoTab® basal insulin algorithm supported an efficacious and safe glycemic management in patients with type 2 diabetes in acute geriatric care.

Disclosure

A. Libiseller: None. K.M. Lichtenegger: None. J. Kopanz: None. A. de Campo: Advisory Panel; Self; Sanofi-Aventis. T. Wiesinger: Other Relationship; Self; Bristol-Myers Squibb Company. L. Weidinger: None. L. Laubreiter: None. W. Schippinger: None. T. Truskaller: None. B.C. Lackner: None. P. Beck: Employee; Self; decide Clinical Software GmbH. Stock/Shareholder; Self; decide Clinical Software GmbH. B. Höll: Employee; Self; decide Clinical Software GmbH. T.R. Pieber: Advisory Panel; Self; ADOCIA, Arecor Limited, AstraZeneca, Novo Nordisk A/S, Sanofi. Speaker's Bureau; Self; Novo Nordisk A/S.

Funding

Austrian Research Promotion Agency (FFG844737)
957-P: Differences in Glucose Level between Left Arm and Right Arm Using Continuous Glucose Monitors
Authors: KIM NICOLE , PHAM KEVIN , SHEK ALLEN , LIM JEREMY J. , SHAH SACHIN A.
Published: June 2019

Background: Continuous glucose monitoring (CGM) is an emerging technology that provides real-time interstitial glucose levels through a sensor with a thin filament inserted under the skin. It is customary for patients to rotate sensor application sites between arms to minimize skin irritation. However, the degree of inter-arm differences with CGM technology remains unknown.

Materials and Methods: Self-proclaimed right-handed (n=5) and left-handed (n=5) subjects, regardless of concurrent comorbidities, were enrolled for CGM. A FreeStyle Libre Pro Flash Glucose Monitoring System sensor was simultaneously placed on each arm for all subjects. The sensors were worn for a maximum of 14 days with measurements taken every 15 minutes. Muscle mass and body fat analysis was conducted using a multi-frequency segmental body composition analyzer. Glucose levels from the right and left arms were time-matched with the first 12 hours eliminated from analysis.

Results: A total of 9830 paired glucose levels were included for analysis. Glucose levels were outside the euglycemic range (70-140 mg/dL) for 14.5% of the time in the right arm compared to 20.0% in the left arm (P<0.001). The glucose level in the right arm was higher than the left arm in 67% (range 46 - 98%) of the measurements (P<0.001). The absolute difference in glucose level between the right and left arms was >10 mg/dL, >20 mg/dL, and >30 mg/dL, for 19.4%, 3.4%, and 0.32% of the time, respectively. The mean glucose level in the right arm was higher than the left arm by 3.75 mg/dL [SD, 8.51 mg/dL] in right-handed subjects, and by 3.84 mg/dL [SD, 7.09 mg/dL] in left-handed subjects (P=0.54).

Conclusion: Time in euglycemic range could significantly differ based on arm selection when using a CGM. While the absolute difference in glucose levels between arms appears small, this degree of variability would have significant implications for clinical trial design using CGM technology. Arm dominance does not explain the inter-arm glucose level discordance.

Disclosure

N. Kim: None. K. Pham: None. A. Shek: None. J.J. Lim: Employee; Self; Genentech, Inc. Stock/Shareholder; Self; F. Hoffmann-La Roche Ltd. S.A. Shah: None.

Funding

University of the Pacific
962-P: Continuous Glucose Monitor Readings Lag Interstitial Glucose by Several Minutes
Authors: MCCLATCHEY PENN MASON , MCCLAIN ETHAN S. , WILLIAMS IAN M. , GREGORY JUSTIN M. , CLIFFEL DAVID , WASSERMAN DAVID
Published: June 2019

Objective: Continuous glucose monitor (CGM) measured glucose lags blood glucose by several minutes, and this delay is usually attributed to the latency of interstitial fluid. However, independent findings suggest that interstitial glucose dispersion is near-immediate. This study seeks to determine the physiological locus of CGM latency.

Methods: Current was measured at 1 Hz in Dexcom G4 CGMs using a CHI 1440 potentiostat. CGMs were exposed to varying glucose levels in vitro to define sensor kinetics. Next, CGMs were implanted in mice, and sensor vs. blood glucose responses were measured following an intravenous glucose challenge. Finally, dispersion of a fluorescent glucose analogue (2-NBDG) into the CGM micro-environment was observed in vivo using intravital microscopy. Tissue deposited on sensors and non-CGM implanted subcutaneous fat were then collected for histological analysis.

Results: The CGM time constant in vitro was 51±3 seconds. In vivo, CGMs took 24±7 minutes to reach a local maximum in maximum current following intravenous injection of glucose. 2-NBDG took 21±7 minutes to reach maximum fluorescence near the sensor, vs. 6±6 minutes in subcutaneous fat (p=0.0011). Collagen was increased in sensor deposits relative to non-CGM implanted subcutaneous fat (p=0.0018), and collagen content was correlated with 2-NBDG latency (R=0.96, p=0.0004).

Conclusions: Interstitial glucose responds to changes in blood glucose within seconds, whereas the glucose levels seen by a CGM are delayed by several minutes due to fibrous encapsulation. A CGM that avoids encapsulation could approximate real-time blood glucose measurements. Additionally, these data raise the possibility that fibrosis underlies insulin resistance by creating a biophysical impediment to the interstitial dispersion of glucose.

Disclosure

P.M. McClatchey: None. E.S. McClain: None. I.M. Williams: None. J.M. Gregory: Consultant; Self; InClinica. D. Cliffel: None. D. Wasserman: None.

Funding

National Institutes of Health (DK059637, DK054902, DK050277, T32DK101003, F32DK120104); American Heart Association
963-P: Impact of Exercise on Glycemia among Users of Flash Glucose Monitoring
Authors: COVINGTON KENDALL , JANGAM SUJIT , DUNN TIMOTHY
Published: June 2019

Real-world data gathered from flash glucose monitoring (FreeStyle LibreTM system) can be used to examine the impact of exercise on time in hypoglycemia, euglycemia, and hyperglycemia. De-identified data from 623 users who recorded 29,280 exercise notes were analyzed, all having notes spanning at least 10 days and more than 25% of days with notes. Glucose time in ranges during night time (11P-8A) and fasting levels (7A-8A) following exercise days and non-exercise days were evaluated. Nights following exercise had an average of 119.2 min/night above 180 mg/dL vs. 137.0 min/night without exercise (p < .001), a difference of 17.8 min/night. There was a median difference of 11.3 fewer minutes in hyperglycemia on exercise nights, with minimal increase in time in hypoglycemia (1.2 min). Days with exercise had an average fasting glucose the next day of 137 + 59 mg/dL compared to fasting levels of 140 + 59 mg/dL without (p < .001). More exercise days had fasting glucose below 70 mg/dL (8.2% vs. 6.4%), but fewer were above 180 mg/dL (19.5% vs. 25.4%). While days with exercise are associated with increased time in hypoglycemia, users saw a greater reduction in hyperglycemia. Flash glucose monitoring can help identify pre-exercise levels and trends which can be used to assist patients in improving time-in-range without increasing nighttime hypoglycemia.

Disclosure

K. Covington: Employee; Self; Abbott Laboratories. S. Jangam: Employee; Self; Abbott Laboratories. Employee; Spouse/Partner; Abbott Laboratories. T. Dunn: Employee; Self; Abbott Laboratories.

Funding

Abbott Diabetes Care
967-P: Association between Visit-to-Visit Blood Pressure Variability and Blood Glucose Control, a Retrospective Analysis of Three Population-Based Cohorts of Diabetic Patients
Authors: ZHANG SHUO , XIE LI , ZHANG WEITUO , QIAN BIYUN
Published: June 2019

Background: Relationship between blood pressure (BP) and blood glucose control is still unsure. This study aimed to find whether visit-to-visit variability (VVV) of BP could be an independent risk factor for blood glucose control.

Methods: A retrospective analysis was conducted in 95254 Chinese primary care diabetic patients from Oct. 2014 to Nov. 2018 with repeated measurements of BP and fasting plasma glucose (FPG) every 3 months, including 3 cohorts: Putuo District diabetes cohort (PTD, N=43590), Xuhui District diabetes cohort (XHD, N=45451) and Changning District diabetes cohort (CND, N=6213). The VVV of systolic blood pressure (SBP) was evaluated by average successive difference (ASD), and glucose control was evaluated by “FPG well-controlled proportion” (FPG≤7mmol/L as “well-controlled”), over 16 follow-ups. Logistic regression was used to explore the association of SBP ASD and FPG well-controlled proportion. The full model adjusted the baseline characteristics considering age, gender, BMI, exercise, medication and diet. Model was built in PTD and validated in the other 2 cohorts.

Results: Patients in PTD can be divided into 3 groups by VVV of SBP, which were “stable,” “fluctuating” and “highly fluctuating” groups. With “stable” group as reference, higher VVV of SBP was associated with lower FPG well-controlled proportion. In PTD, “fluctuating” group, OR: 1.18, 95% CI: 1.13-1.24; “highly fluctuating” group, OR: 1.42, 95% CI: 1.35-1.49. Results were consistent in the 2 validated cohorts and ORs for the “highly fluctuating” group were 2.37, 95% CI: 2.23-2.52 and 1.34, 95% CI: 1.15-1.56. In the whole population (N =95254), “highly fluctuating” group was associated with the worst blood glucose control with OR: 1.67, 95% CI: 1.62-1.73.

Conclusion: High VVV of SBP could be an independent risk factor for blood glucose control, which suggests extra surveillance for those with highly fluctuating SBP.

Disclosure

S. Zhang: None. L. Xie: None. W. Zhang: None. B. Qian: None.
973-P: The Feasibility of Continuous Glucose Monitoring in Youth with Type 2 Diabetes Mellitus: A Pilot Study
Authors: LAROCHE ALLISON , UTZSCHNEIDER KRISTINA , PIHOKER CATHERINE
Published: June 2019

Background: Continuous glucose monitoring (CGM) systems are increasing in use among youth with type 1 diabetes; however, the feasibility of wearing an unblinded, real-time CGM has not been studied in youth with type 2 diabetes. The aims of this study are to evaluate the feasibility of CGM and to explore its effects on lifestyle behaviors and glycemic control among youth with type 2 diabetes.

Methods: Youth ages 12-19 years (n=17) with type 2 diabetes participated in a prospective cohort intervention study with Dexcom G6 CGM. For baseline data, participants wore a blinded CGM for the 1st week and unblinded CGM for the 2nd and 6th weeks. Glycemic and metabolic measures (glucose, insulin, c-peptide, hemoglobin A1C, fructosamine, 1,5 anhydroglucitol, lipids), 24-hour food recalls, and Youth Eating Disorder Questionnaires (YEDE-Q) were collected at baseline and conclusion of study.

Results: Eight participants have completed the study to date (4F/4M, mean age 15.7 years, BMI mean 32.6 kg/m2); data on metabolic measures, food recall, YEDE-Q are pending. See Table 1.

Conclusion: Based on this pilot study, CGM use among youth with type 2 diabetes is feasible and acceptable. Larger studies of longer duration are needed to demonstrate whether real-time CGM can improve glycemic control and lifestyle behaviors in this population.

Disclosure

A. LaRoche: None. K. Utzschneider: Consultant; Self; Novo Nordisk Inc. C. Pihoker: None.

Funding

National Institutes of Health (DK007247)
98-LB: Efficacy and Clinical Considerations of Flash Glucose Monitoring among Canadian Adults with Diabetes
Authors: JAZDAREHEE ARIA , KAPELUTO JORDANNA E. , PAWLOWSKA MONIKA , PAWLOWSKA MONIKA , MACKENZIE-FEDER JESSICA , SCHROEDER BENJAMIN , WHITE ADAM
Published: June 2019

The FreeStyle Libre flash glucose monitoring (FGM) system was introduced in Canada in 2017. FGM incorporates aspects of conventional blood glucose testing and continuous glucose monitoring (CGM). The FreeStyle Libre utilizes a sensor placed on the upper arm to test interstitial glucose levels every fifteen minutes and may be used to obtain a ‘flash’ (real-time) reading. The FreeStyle Libre system is less costly than CGM and may help overcome common barriers to conventional glucose testing, including pain, needle aversion, and inconvenience, but at present there is no public coverage for the product available in Canada. In this study, we investigated the continuation rate of FGM use in patients provided complimentary FreeStyle Libre starter packs, consisting of one reader and two 14-day sensors, and the impact of continued use on glycemic control. More than half (21/40) continued to use the system beyond the trial period. The mean A1C of patients who continued using FGM decreased by 1.14% at three months, compared to an increase of 0.17% in those who did not (p=0.038). Despite small sample size, there was also a trend of decreased basal and bolus insulin requirements in patients continuing use. These results show benefit and support the implementation of public coverage because greater adherence of self-monitoring of blood glucose results in improved glycemic control which ultimately reduces the risk of long-term diabetes associated complications and healthcare costs.

Disclosure

A. Jazdarehee: None. J.E. Kapeluto: None. M. Pawlowska: None. J. MacKenzie-Feder: None. B. Schroeder: Advisory Panel; Self; Abbott, Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc., LifeScan Canada, Natesto, Sanofi-Aventis. Speaker’s Bureau; Self; AstraZeneca, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc. A. White: Advisory Panel; Self; Abbott, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc., Sanofi. Speaker’s Bureau; Self; Merck & Co., Inc. Other Relationship; Self; Diabetes Canada.
994-P: Effects of Dulaglutide and Trelagliptin on ß-Cell Function after One Year in Patients with Type 2 Diabetes: DUET-Beta Extension Study
Authors: KONDO YOSHINOBU , SATOH SHINOBU , TERAUCHI YASUO
Published: June 2019

Studies on glucagon-like peptide 1 receptor agonists (GLP-1-RA) and dipeptidyl peptidase-4 inhibitors (DPP-4-I) have suggested positive effects on beta-cells. However, direct comparison between weekly GLP-1-RA: dulaglutide (Dula) and weekly DPP-4-I: trelagliptin (Trela) effects on beta-cell function is not known. Thus, we compared the effects of Dula and Trela on beta-cell function in patients with type 2 diabetes (T2D) in an open-label, parallel-group, randomized controlled 24-week trial, with an additional 28 weeks of extension follow-up. Beta-cell function and insulin resistance were assessed by HOMA2-%β and HOMA2-IR, respectively. Body composition was also assessed by the bioelectrical impedance method. Fifty metformin ± basal insulin-treated patients with type 2 diabetes (T2D) were randomized to Dula 0.75 mg/week or Trela 100 mg/week. Forty-eight patients entered the 28-weeks extension study and continued their allocated therapy. Forty-six completed (Dula: n = 23, Trela: n = 23) the 52-week exposure and were included in the analysis. HOMA2-%β was higher in the Dula group than in the Trela group throughout the 52 weeks (24 weeks: Dula: +50.6 ± 9.9% vs. Trela: +7.5 ± 9.5%, p = 0.003; 52 weeks: Dula: +54.5 ± 8.7% vs. Trela: +3.0 ± 8.7%, p <0.001). HOMA2-IR was not significantly different between both groups (24 weeks: Dula: -0.29 ± 0.50 vs. Trela: -0.19 ± 0.50, p = 0.88; 52 weeks: Dula: +0.79 ± 0.52 vs. Trela: +0.45 ± 0.52, p = 0.64). At 52 weeks, HbA1c had a larger decrease with the Dula group (Dula: -0.52 ± 0.11%, Trela: -0.13 ± 0.12%, p = 0.02). Body fat mass was reduced more in the Dula group than the Trela group (52 weeks: Dula: -1.5 ± 0.3 kg vs. Trela: +0.4 ± 0.3 kg, p <0.001). However, Dula patients did not have a reduction in skeletal muscle mass (52 weeks: 0.0 ± 0.3 kg, p = 0.94).

In conclusion, Dula increased HOMA2-%β level more than Trela over the 52 weeks, without a significant change in HOMA2-IR level. Dula reduced body fat mass without skeletal muscle mass loss.

Disclosure

Y. Kondo: None. S. Satoh: None. Y. Terauchi: Advisory Panel; Self; AstraZeneca, Daiichi Sankyo Company, Limited, Eli Lilly and Company, Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novo Nordisk A/S, Sanofi. Research Support; Self; Daiichi Sankyo Company, Limited, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novartis Pharmaceuticals Corporation, Novo Nordisk A/S, Ono Pharmaceutical Co., Ltd., Sanofi, Shionogi & Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd. Speaker's Bureau; Self; Astellas Pharma Inc., AstraZeneca, Bayer Yakuhin, Ltd., Daiichi Sankyo Company, Limited, Eli Lilly and Company, Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novartis Pharmaceuticals Corporation, Novo Nordisk A/S, Ono Pharmaceutical Co., Ltd., Sanofi, Sanwa Kagaku Kenkyusho, Shionogi & Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd.
998-P: Efficacy and Safety of Semaglutide by Baseline BMI in SUSTAIN 1-5 and 7
Authors: VILJOEN ADIE , FRIAS JUAN P. , GONDOLF THEIS , HANSEN OLUF , WIJAYASINGHE NELUN , UNGER JEFF
Published: June 2019

Semaglutide, a once-weekly glucagon-like peptide-1 analog for type 2 diabetes, showed significant, clinically meaningful reductions in HbA1c and body weight in the SUSTAIN clinical trial program. Higher body mass index (BMI) at baseline (BL) was associated with greater weight loss during semaglutide therapy. This post hoc analysis evaluated change in HbA1c by BL BMI (<25, 25-<30, 30-<35 and ≥35 kg/m2) for semaglutide vs. comparators by trial for SUSTAIN 1-5 and 7. Safety data were pooled and analyzed stratified by trial. Reductions in mean HbA1c (%) from BL were greater in all BMI subgroups with semaglutide vs. comparators (Figure). There were no significant interactions between treatment and BMI, indicating a consistent effect of semaglutide vs. comparator on change in HbA1c across BMI subgroups. In all treatment arms, adverse events (AEs) occurred in a similar proportion of subjects across BMI subgroups. Gastrointestinal AEs were higher with semaglutide, but decreased with increasing BL BMI, vs. comparators (semaglutide: <25 kg/m2=48.8%, 25-<30 kg/m2=43.0%, 30-<35 kg/m2=39.4% and ≥35 kg/m2 =39.3% vs. comparators range: 21.2-28.9%). Premature treatment discontinuation due to AEs was higher in all BMI subgroups with semaglutide vs. comparators (5.6-15.3% vs. 2.3-8.3%).

In conclusion, the efficacy of semaglutide in lowering HbA1c does not appear to be influenced by BL BMI. Semaglutide had an acceptable safety profile in all BMI subgroups.

Disclosure

A. Viljoen: Advisory Panel; Self; NAPP Pharmaceuticals Limited. Research Support; Self; Eli Lilly and Company, Sanofi-Aventis. Speaker's Bureau; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Novo Nordisk A/S. J.P. Frias: Advisory Panel; Self; Becton, Dickinson and Company, Eli Lilly and Company, Gilead Sciences, Inc., Sanofi. Consultant; Self; Echosens, Genentech, Inc., Johnson & Johnson Diabetes Institute, Novo Nordisk Inc., Zafgen, Inc. Research Support; Self; AbbVie Inc., Akcea Therapeutics, Allergan, Amgen Inc., AstraZeneca, Bayer US, Boehringer Ingelheim Pharmaceuticals, Inc., Bristol-Myers Squibb Company, Cirius Therapeutics, Elcelyx Therapeutics, Inc., Eli Lilly and Company, Enanta Pharmaceuticals, Inc., GENFIT, Intarcia Therapeutics, Inc., Intercept Pharmaceuticals, Inc., Ionis Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., NGM Biopharmaceuticals, Novartis Pharmaceuticals Corporation, Novo Nordisk A/S, Oramed Pharmaceuticals, Pfizer Inc., Sanofi, TaiwanJ Pharmaceuticals Co., Ltd., Theracos, Inc. Speaker's Bureau; Self; Merck & Co., Inc., Sanofi. T. Gondolf: Employee; Spouse/Partner; Chr. Hansen Holding A/S. Employee; Self; Novo Nordisk A/S. O. Hansen: Employee; Self; Novo Nordisk A/S. N. Wijayasinghe: None. J. Unger: Advisory Panel; Self; Abbott Laboratories, Novo Nordisk Inc. Speaker's Bureau; Self; Janssen Pharmaceuticals, Inc.

Funding

Novo Nordisk A/S
The Influence of Type 2 Diabetes–Associated Factors on Type 1 Diabetes
Authors: Redondo Maria J. , Evans-Molina Carmella , Steck Andrea K. , Atkinson Mark A. , Sosenko Jay
Published: June 2019

Current efforts to prevent progression from islet autoimmunity to type 1 diabetes largely focus on immunomodulatory approaches. However, emerging data suggest that the development of diabetes in islet autoantibody–positive individuals may also involve factors such as obesity and genetic variants associated with type 2 diabetes, and the influence of these factors increases with age at diagnosis. Although these factors have been linked with metabolic outcomes, particularly through their impact on β-cell function and insulin sensitivity, growing evidence suggests that they might also interact with the immune system to amplify the autoimmune response. The presence of factors shared by both forms of diabetes contributes to disease heterogeneity and thus has important implications. Characteristics that are typically considered to be nonimmune should be incorporated into predictive algorithms that seek to identify at-risk individuals and into the designs of trials for disease prevention. The heterogeneity of diabetes also poses a challenge in diagnostic classification. Finally, after clinically diagnosing type 1 diabetes, addressing nonimmune elements may help to prevent further deterioration of β-cell function and thus improve clinical outcomes. This Perspectives in Care article highlights the role of type 2 diabetes–associated genetic factors (e.g., gene variants at transcription factor 7-like 2 [TCF7L2]) and obesity (via insulin resistance, inflammation, β-cell stress, or all three) in the pathogenesis of type 1 diabetes and their impacts on age at diagnosis. Recognizing that type 1 diabetes might result from the sum of effects from islet autoimmunity and type 2 diabetes–associated factors, their interactions, or both affects disease prediction, prevention, diagnosis, and treatment.