bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated Integration of Glucometer Data into the Electronic Health Record
Authors: WEATHERLY JAKE , KISHNANI SANIYA S. , AYE TANDY 
Published: June 2018

Pediatric patients and families with T1D have difficulty meeting target HgbA1c. Providers encourage families to contact them with BG values between visits. Currently it is cumbersome to share BG with clinical providers creating a barrier to communication. Many applications on smart phones exist to help patients track BG, but most do not integrate with the electronic health record (EHR). Recent advances in technology can integrate the glucometer data into the EHR. This pilot and feasibility study aims to understand if automated integration of glucometer data into the EHR along with remote monitoring by health care providers improve patient initiated provider communication, self-management and HgbA1c. Patients and families with type 1 diabetes and an Apple iPod or iPhone (5s or higher) were invited to participate. Participants enrolled in MyChart, downloaded the HealthKit application and connected the AccuChek Aviva Connect (Roche) glucometer to the HealthKit. Through the EHR, BG values were automatically integrated to the EHR via WiFi. The number of patient initiated contacts via telephone or MyChart messages between clinic visits were recorded during each of the 3 phases (lasting 3 months each): run-in phase (providers waited for participant initiated phone calls), intervention phase (provider initiated calls to participants to review BG trends) and learned phase (providers waited for participant initiated phone calls). The number of participant initiated calls for each phase was measured and compared using a paired t test. Thirty-two families (mean Hgb A1c 8.5 ± SD 1.7, mean age 13.9 years ± SD 3.8) completed all phases. Despite automated integration of BG values and guided review of BG trends, the number of patient initiated calls (p=0.17) and HgbA1c values (p=0.67) did not improve. Families did felt safer knowing providers had automated integration of BG values into the EMR. However, barriers to adoption and effectiveness of this technology exist and patient motivation is still needed.

Disclosure
J. Weatherly: None. S.S. Kishnani: None. T. Aye: None.
Randomized, Multicentric, Prospective Study Assessing the Effect of Adding VILDagliptin to Insulin in Hemodialysed Type 2 Diabetic Patients—The VILDDIAL Study
Authors: MUNCH MARION , SMAGALA AGNÈS , MEYER LAURENT , MEYER NICOLAS , VERIER ODILE , FLEURY DOMINIQUE I. , GUERCI BRUNO , HANNEDOUCHE THIERRY , CRIDLIG JOELLE , BALTZINGER PHILIPPE , DUCLOUX DIDIER , BOROT SOPHIE , KUNZ KRISTIAN W. , ALENABI FARIDEH , WINISZEWSKI PATRICE , CHANTREL FRANCOIS , KESSLER LAURENCE 
Published: June 2018

Introduction: Type 2 diabetic patients (T2D) undergoing chronic dialysis are at very high cardiovascular risk, requiring optimization of insulin treatment.
Vildagliptin (Galvus), can be used in severe and terminal renal impairment (eGFR&lt;30 or &lt;15ml/min/1,73m²) and protects against hypoglycemia.
The aim of this study was to evaluate the efficacy and safety of vildagliptin (50 mg/d) on top of insulin in hemodialysed T2D.
Methods: Sixty-five patients treated with multiple daily insulin injections were randomized (group 1-G1, n=33-usual insulin regimen, group 2-G2, n=32-addition of vildagliptin) in a multicentric, prospective, controlled, open-label, 12 weeks- study. Glucose control was evaluated using Continuous Glucose Monitoring (CGM, I-Pro Medtronic) for 48h, at Week 1 (W1) and Week 12 (W12), HbA1c and daily insulin needs. Statistical analysis was performed in intention to treat.
Results: Both groups were comparable (G1 vs. G2) for age (71.3 ± 7.4 vs. 69.7 ± 9.6 years), BMI (32.1 ± 6 vs. 34.5 ± 6.4 kg/m²), daily doses of insulin (47.7 ± 29.2 vs. 51.3 ± 25.4 IU), HbA1c (7.3 ± 1.1 vs. 7.3 ± 1.1%), and mean glucose at W1 (using CGM, 167.6 ± 58.3 vs. 149.4 ± 76.1 mg/dl). At W12 compared to W1, mean glucose decreased significantly only in G2 (149.4 to 136.9 mg/dl, p &lt;0.05). At W12 in G2 vs. G1, time in range (70-180 mg/dl) was greater (77.2 vs. 69.5%, p &lt;0.05), time spent &gt; 180 mg/dl was lower (21.27 vs. 39.03%, p &lt;0.05), there was no difference in time spent &lt;70 mg/dl (1.54 vs. 1.45%, NS). At W12, HbA1C and insulin requirements were respectively 6.9 ± 0.9% vs. 7.2 ± 1.1% (NS) and 49 ± 32 IU/d vs. 46 ± 23 IU/d (NS). There was no difference in adverse events between both groups. Overall, 8 symptomatic hypoglycemia occurred in G1 and 13 in G2.
Conclusion: Adding vildagliptin (50 mg/d) to insulin in hemodialysed T2D improves glycemic control by increasing time spent in normoglycemia and reducing time in hyperglycemia, without increasing time in hypoglycemia.

Disclosure
M. Munch: None. A. Smagala: None. L. Meyer: None. N. Meyer: None. O. Verier: None. D.I. Fleury: None. B. Guerci: None. T. Hannedouche: None. J. Cridlig: None. P. Baltzinger: None. D. Ducloux: None. S. Borot: Consultant; Self; Abbott, Animas Corporation, Johnson & Johnson Diabetes Institute, LLC., Medtronic, Roche Diabetes Care Health and Digital Solutions. K.W. Kunz: None. F. Alenabi: None. P. Winiszewski: Other Relationship; Self; Abbott, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk Inc., Eli Lilly and Company, Sanofi. F. Chantrel: None. L. Kessler: None.
Metabolic Markers Associated with Increased Risk for Progression from Single to Multiple Autoantibodies in Type 1 Diabetes Relatives
Authors: BOSI EMANUELE , GEYER SUSAN , SOSENKO JAY , BECKER DOROTHY J. , BATTAGLIA MANUELA , ISMAIL HEBA M. , ATKINSON MARK A. , PHILIPSON LOUIS H. , PALMER JERRY P. , EVANS-MOLINA CARMELLA 
Published: June 2018

Stage 1, defined as having ≥2 (i.e., multiple) autoantibodies (MA+) with normoglycemia, is currently identified as the earliest pathogenetic phase of type 1 diabetes. There is limited information of a possible β-cell function decline during the preceding phase, defined as single autoantibody positivity (SA+). Therefore, we evaluated metabolic measures at time of SA+ determination and assessed their predictive value for progression to MA+. Relatives participating in the TrialNet prospective Pathway to Prevention with SA+ (GADA, IAA, or IA-2A) were studied. Time from SA+ to MA+ was analyzed to assess risk of conversion. Measures evaluated during OGTT included: mean AUC for C-peptide, glucose, and insulin; fasting glucose and C-peptide; early insulin and C-peptide responses (30-0 min); peak C-peptide; glucose and C-peptide sums (30 to 120 min) and Index60 (incorporating fasting C-peptide, 1-hour glucose, and 1-hour C-peptide). Clinical factors included age, sex, ethnicity and autoantibody type. Of the 2,265 subjects identified as SA+, 57% were female, 83% white, 14% hispanic, with a median age of 14.8 years (range: 1.0 to 48.7 years). Most (71%) had GADA, 25% IAA and 4% IA-2A; 68% were tested also for ZnT8A. Overall, 407 (18%) SA+ progressed to MA+ during a median follow-up of 1.8 years. Adjusting for age, sex, and ethnicity we found that higher levels of mean AUC glucose (HR=2.4, p=0.004) significantly increased risk of progression to MA+. Index60 was significant both continuous (HR=1.17, p=0.004) and dichotomized (≥1 vs. &lt;1; HR=2.28, p&lt;0.0001). Reduced early C-peptide response was also significantly predictive (HR=0.94, p=0.007). Overall, we found that in early phase of SA+ there are already metabolic markers of beta cell dysfunction associated with increased risk of progression to MA+. These findings provide insights into early phases of the disease and provide the rationale for interventions at this stage.

Disclosure
E. Bosi: Consultant; Self; Abbott, AstraZeneca, Lexicon Pharmaceuticals, Inc., LifeScan, Inc.. Employee; Spouse/Partner; LifeScan, Inc.. Consultant; Self; Medtronic, Merck Sharp & Dohme Corp., Novartis AG, Roche Diabetes Care Health and Digital Solutions, Sanofi. S. Geyer: None. J. Sosenko: None. D.J. Becker: None. M. Battaglia: None. H.M. Ismail: None. M.A. Atkinson: Other Relationship; Self; Patent Issued. L.H. Philipson: Research Support; Self; Novo Nordisk Inc.. Speaker's Bureau; Self; Merck & Co., Inc.. Research Support; Self; JAEB Center For Health Research, JDRF, Janssen Pharmaceuticals, Inc., Medtronic MiniMed, Inc.. J.P. Palmer: None. C. Evans-Molina: None.
Pharmacokinetic and Pharmacodynamic Properties of a Novel "Superfast" Insulin Aspart Formulation
Authors: PIEBER THOMAS R. , HOWELL SARAH J. , JEZEK JAN , GERRING DAVID J. 
Published: June 2018

Background: An insulin with a significantly faster onset of action compared to current state-of-the-art products will improve postprandial glycaemic control, reduce variability, improve HbA1c and increase flexibility. A superfast insulin is a key component for the development of efficient closed-loop pump systems.
Method: A superfast formulation, utilising excipients with metal ion binding constants critical in controlling injection site absorption, was investigated in diabetic mini swine in a randomized, cross-over experiment. Ten male animals received 0.2 U/kg insulin via subcutaneous injection. At standardized intervals post-injection, blood glucose and insulin were measured.
Results: Pharmacodynamic (PD) and pharmacokinetic (PK) properties of Superfast insulin were compared to an ultra-rapid control composition including nicotinamide. Tmax (22.5 minutes vs. 41.9 minutes) and T1/2max (7.9 minutes vs. 11.9 minutes) were significantly (P&lt;0.01 and P&lt;0.02) shorter for Superfast insulin, with a correspondingly faster glucose lowering action (Figure 1). Figure 1a) PD and 1b) PK profile of 100U/mL Superfast insulin (red) in comparison to an ultra-rapid acting insulin control containing nicotinamide (blue)1a.
Conclusion: This novel formulation represents a unique superfast prandial insulin that more closely matches a physiological response to blood glucose with the potential to be a major advancement for diabetes care.

Disclosure
T.R. Pieber: Consultant; Self; Arecor, AstraZeneca, Eli Lilly and Company, Novo Nordisk A/S, Sanofi. Employee; Self; CBmed. Research Support; Self; Novo Nordisk A/S, AstraZeneca. S.J. Howell: None. J. Jezek: None. D.J. Gerring: Employee; Self; Arecor Limited.
Identification of Cross Reactive Insulin Immunogenic Epitopes from Commensal Gut Microbes
Authors: ALTINDIS EMRAH , VOMUND ANTHONY N. , CHOW I-TING , DAMASIO MARCOS , KWOK WILLIAM , UNANUE EMIL R. , KAHN C. RONALD 
Published: June 2018

Type 1 diabetes (T1D) is a chronic autoimmune disease characterized by destruction of pancreatic β-cells. Because studies suggest an environmental factor for the initiation of this autoimmunity, we hypothesized that intestinal microbes might carry peptides with sequences similarity to insulin, a major autoantigen. These peptides could modify the autoimmune response through molecular mimicry. Using a bioinformatics approach, we identified seventeen B:9-23 epitope-like sequences in different microbial proteins and investigated their capacity to stimulate insulin-specific reactive T cells obtained from T1D patients and NOD mice. In particular Parabacteroides distasonis contained a segment in the hypoxanthine phosphoribosyltransferase (HPRT) protein with high sequence similarities to the B:9-23 segment of the insulin B chain. When synthesized, this peptide stimulated both human and selected NOD mice T cells that recognized the B:9-23 peptide. Conversely, lymphocytes from mice immunized with this P. distasonis peptide showed increased secretion of cytokines when stimulated with the insulin B:9-23 peptide. P. distasonis is a commensal microbe in the human gut microbiome. Analysis of T1D metagenomic gut microbiome data from Finland, Estonia and Russia showed that the abundance of P. distasonis significantly differed between controls and seroconverting islet antibody-positive children at risk for T1D depending on the location of the study. In on-going analysis, compared to healthy control individuals, sera from T1D patients showed a significant antibody response against multiple P. distasonis proteins.
In summary, a peptide from HPRT of P. distasonis, a normal component of the gut microbiome, can elicit T-cell responses cross-reactive with human insulin-related peptides. We hypothesize that such cross-reactive peptides may become accessible to intestinal phagocytes for their presentation to T cells thus modulating T1D in genetically susceptible individuals.

Disclosure
E. Altindis: None. A.N. Vomund: None. I. Chow: None. M. Damasio: None. W. Kwok: None. E.R. Unanue: None. C. Kahn: Advisory Panel; Self; CohBar, ERX Therapeutics, AntriaBio, Inc.. Board Member; Self; Kaleio Biosciences.
Real-World Avoidance of Glucose Excursions with the Guardian Connect CGM System’s Predictive Alerts
Authors: COHEN OHAD , ABRAHAM SINU BESSY , MCMAHON CHANTAL M. , AGRAWAL PRATIK , VIGERSKY ROBERT 
Published: June 2018

The Medtronic GuardianTM Connect continuous glucose monitoring (CGM) system, with predictive high and low glucose alerts, allows users to view sensor glucose (SG) data on a smartphone that notifies them 10-60 minutes before an excursion. The rates of alerts and outcomes of users on the GuardianTM Connect system were evaluated. We identified 2,541 users with &gt;5 days of SG data in the CareLinkTM database from Jan 2, 2017-Sep 5, 2017 (data collected Jan 2, 2017-Dec 14, 2017). Excursions were identified when SG values were beyond the users’ preset SG threshold limit for ≥15 min. As a control, alerts were simulated during user alert-disabled periods to compare excursion frequency. Excursion durations following the alert times were segmented into avoided, ≤20min, 20-60 min, and &gt;60min. Simulated SG limits were 202mg/dL (11.2mmol/L) for high and 70mg/dL (3.9mmol/L) for low. Simulated predictive times before excursions were 12.5min for high and 17.5min for low. The Table shows percentages of each alert resulting in an excursion. Users who enabled predictive alerts avoided 60% of low and 39% of high events. The percentage point improvement for excursions avoided was 28% and 31% following predicted low and high alerts vs. control. Stand alone CGM technology, like the GuardianTM Connect system, with predictive alerts are useful for tracking SG and enabling timely actions that help avoid high and low excursions.

Disclosure
O. Cohen: Employee; Self; Medtronic. S. Abraham: None. C.M. McMahon: Employee; Self; Medtronic MiniMed, Inc.. P. Agrawal: None. R. Vigersky: Employee; Self; Medtronic MiniMed, Inc..
Use of Real Time Continuous Glucose Monitoring (CGM) in Real Life Clinical Practice Compared with Finger-Stick Glucose Monitoring
Authors: TOSCHI ELENA , SLYNE CHRISTINE , ATAKOV-CASTILLO ASTRID , GREENBERG JORDAN , GREAVES TORI , CARL SAM P. , MUNSHI MEDHA 
Published: June 2018

Background: Studies have shown benefits of CGM in management of type 1 diabetes (T1D), however, it is not yet clear if the benefits are maintained in a real-life clinical practice.
Methods: We evaluated 2 groups of adults (age 18-35 years) with T1D: One using CGM and the other using glucometer. Patients underwent either 2-week blinded CGM or assessment from personal CGM over 2 weeks. Patients also completed questionnaires for demographics, surveys for fear of hypoglycemia (HSF II), hypoglycemic unawareness, and problem areas in diabetes Questionnaire (PAID)). Glycemic control was measured by A1c.
Results: We evaluated 33 patients withT1D (15 using CGM and 18 using glucometer); average age 27 years, duration of diabetes 10 years, years of education 7 years. CGM users had better glycemic control as seen by A1C of 7.5% vs. 8.5 (p&lt;0.05). 40% of patients using CGM had clinically significant hypoglycemia (glucose≤54 for &gt; 15 mins/episode) compared to 66% in glucometer-users (mean duration 18 min/day vs. 27 min/day) (p=ns). CGM users spent longer time in range (70-180 mg/dl) compared to glucometer-users (14 hours/day vs. 12 hours/day). Hypoglycemia unawareness was reported by 44% of CGM users compared to 14% of glucometer-users. More CGM-users reported checking SMBG &gt; 6 times per day, and worried more regarding nocturnal hypoglycemia (81% vs. 57%). Diabetes-related distress was lower in RT-CGM users (PAID score 27 vs. 34).
Conclusion: In a clinical setting, despite more hypoglycemic unawareness and more worry regarding hypoglycemia, T1D patients who used RT-CGM had better glycemic control, higher time spent in range, and lower risk of hypoglycemia compared to glucometer-users.

Disclosure
E. Toschi: None. C. Slyne: None. A. Atakov-Castillo: None. J. Greenberg: None. T. Greaves: None. S.P. Carl: None. M. Munshi: Consultant; Self; Sanofi.
Glucose-Responsive Insulin Delivery with Fast Response Using Charge-Switchable Polymeric Depot
Authors: GU ZHEN 
Published: June 2018

The conventional method for controlling blood glucose levels (BGLs) involves the repeated subcutaneous injection of insulin, which is challenging to achieve satisfied blood glucose regulation and could often lead to hypoglycemia. Therefore, the development of self-regulated, glucose-triggered insulin delivery systems has attracted considerable attentions these years. Herein, we describe a new glucose-responsive insulin delivery formulation with ultrafast response both in vitro and in vivo, dependent on a glucose-triggered charge switch of a cationic polymer. A polymer (designated as poly(EDAA0.4-FPBA0.6)) with pendant amine and 4-carboxy-3-fluorophenylboronic acid was synthesized. Next, a stable complex with high insulin loading capacity (50%) and slow basal insulin release rate was prepared from an equal weight of insulin and polymer in PBS at 7.4. The addition of F-insulin to glucose solution led to decrease of the glucose level by 10 and 30 mg/dL for 100 and 400 mg/dL glucose solution, respectively, within 10 min. Meanwhile, the ζ-potential of poly(EDAA0.4-FPBA0.6) nanoparticles was decreased from the original +40 mV in the absence of glucose, to +22 mV in 100 mg/dL glucose solution, and further decreased to less than +8 mV in 400 mg/dL glucose solution. As a consequence, the insulin release kinetic from the complexes was highly glucose dependent. The in vivo therapeutic efficacy of F-insulin was evaluated in type 1 diabetic mouse model induced by streptozotocin. The BGLs of F-insulin treated group decreased to below 200 mg/dL within 30 min, and further maintained BGLs within the normal range (&lt; 200 mg/dL) for more than 8 hours.

Disclosure
Z. Gu: None.
Learning to Close the Loop
Authors: CROCKET HAMISH 
Published: June 2018

The Do-It-Yourself (DIY) Artificial Pancreas System (APS) community is growing very rapidly. Initially the community was composed of members with expertise in coding and other aspects of computing. It now includes a significant proportion of members without such expertise. Building and operating a DIY APS is a complex task that, in contrast to longer established and FDA-approved therapies, is not supported by Health Care Practitioners. In the absence of professional support, how do novices learn to build and maintain a DIY APS?
Objective: To identify the key enabling and constraining factors associated with learning to build and operate a DIY APS.
Method: A Participatory Action Research approach was used. Key members of the DIY APS community were consulted in developing this study about learning in relation to DIY APS. In-depth, semi-structured interviews were conducted with 10 users of DIY APS who had built their systems in 2017. The participants in this first cohort were from multiple countries. All were adult (18+) type 1 diabetics. A thematic analysis of the interview transcripts was undertaken.
Results: Five key themes were identified. 1) Learning was a continuation and deepening of participants’ previous peer-to-peer learning. 2) Persistence was central to all participants’ learning. 3) Learning was enmeshed with emotional and social support from the DIY APS community. 4) Learning challenges varied considerably between individuals and different DIY systems. 5) Learning extended beyond acquiring technical skills to incorporate the collaborative, ‘pay-it-forward’ ethos of the DIY APS community.
Conclusion: Preliminary findings suggest that learning in this technologically complex field is facilitated by social and relational practices between peers. Commercial and DIY hybrid and closed loop APSs will become increasingly common: it is therefore important to consider how people with diabetes learn to operate these systems, including the potential of peer-to-peer learning.

Disclosure
H. Crocket: None.
Evaluating Human Factors and Design Variables on Pen Injection Technique
Authors: RINI CHRISTOPHER , ROBERTS BRUCE C. , MOREL DIDIER , KLUG RICK , SELVAGE BENJAMIN L. , PETTIS RONALD J. 
Published: June 2018

All parenteral delivery systems require a device/tissue site interface, where design, human factors, and tissue biomechanics impact successful function for consistent, reliable delivery. Limited published data exists quantifying these factors and their interaction effects on delivery success. This in vivo study examines pen needle (PN) hub design and skin application force impacts on needle penetration depth (NPD).
PN injections (2µl contrast agent) were administered to Yorkshire swine flanks at clinically measured skin application forces (0.25, 0.75, 1.25 and 2.0 lbf). In vivo NPD from four 32G x 4mm PN devices (investigational BD Nano™ PRO and three commercial PN devices; n=75/device/force, 1200 total) were measured by fluoroscopic imaging.
The BD Nano™ PRO more closely achieved the 4mm target NPD with significantly less variability (p&lt;0.006) than the commercial PN devices across injection forces. Estimates of IM injection potential, calculated using human tissue measurements, displayed a commensurate reduction in IM risk.
Quantifiable differences observed between PN devices indicate that design features, coupled with aspects of variable use, influence injection consistency. The BD Nano™ PRO design may reduce impact of unintended individual injection technique differences and IM injection potential, which could decrease insulin absorption variability leading to more consistent glycemic control.

Disclosure
C. Rini: Employee; Self; Becton, Dickinson and Company. B.C. Roberts: Employee; Self; Becton, Dickinson and Company. D. Morel: Employee; Self; Becton, Dickinson and Company. R. Klug: None. B.L. Selvage: None. R.J. Pettis: Employee; Self; Becton, Dickinson and Company. Stock/Shareholder; Self; Becton, Dickinson and Company. Research Support; Self; JDRF. Board Member; Self; JDRF.