bluuebunny/crossref_metadata_2025_split huggingface dataset:
829-P: Socioeconomic Barriers to T2D Management: Patient Perspectives on Opportunities for Intervention
Authors: STOTZ SARAH A. , RICKS KATHARINE A.B. , EISENSTAT STEPHANIE A. , WEXLER DEBORAH J. , BERKOWITZ SETH A. 
Published: June 2020

Socioeconomic status (SES) challenges are common barriers to type 2 diabetes (T2D) management. We sought to obtain patient perspectives on T2D interventions sensitive to SES barriers. We conducted focus groups (n=8) with a socioeconomically diverse sample of adults with T2D (n=53). Focus groups used a semi-structured moderator guide and were recorded and transcribed verbatim. We utilized the constant-comparison method for qualitative content analysis. Findings revealed three primary themes: 1) gaps in food and nutrition resources, 2) challenges to healthful eating, and 3) potential interventions to address barriers noted in Themes 1 and 2. Supporting categories for Theme 1 included: lack of structured food resource management education (e.g., healthy eating on a limited budget), need for peer-based or health coaching support specific to low SES circumstances (e.g., facing trade-offs between buying food and paying for medications), and mixed experiences with nutrition counseling (e.g., lack of sensitivity to challenges of healthy eating, behavior change). Supporting categories for Theme 2 included challenges related to stress, mental health, comorbidities, and insufficient food insecurity interventions. To help overcome these barriers, participants described three broad categories of preferred intervention strategies. First, they endorsed SES-informed lifestyle intervention (e.g., focusing on food resource management, sensitive health coaching and nutritional counseling). Second, they expressed enthusiasm for group-based learning opportunities, such as cooking classes and support groups conducted among peers with similar SES. Third, they suggested provision of additional resources such as meal delivery programs, healthy food subsidies, and assistance in accessing food aid. These interventional strategies can be tested and refined to help improve health for disadvantaged individuals with T2D.

Disclosure
S.A. Stotz: None. K.A.B. Ricks: None. S.A. Eisenstat: None. D.J. Wexler: Other Relationship; Self; Novo Nordisk A/S. S.A. Berkowitz: None.

Funding
DK109200, DK092923
845-P: Changes in Plasma Levels of Liver Enzymes in Response to Dapagliflozin, Metformin, or Exercise in People with Prediabetes: The PRE-D Trial
Authors: BLOND MARTIN B. , CLEMMENSEN KIM K. , AMADID HANAN , NIELSEN LEA B. , RIED-LARSEN MATHIAS , KARSTOFT KRISTIAN , PERSSON FREDERIK , JØRGENSEN MARIT E. , FAERCH KRISTINE 
Published: June 2020

Background: Plasma levels of liver enzymes are elevated in prediabetes but may improve in response to weight loss and glucose-lowering interventions. We examined the effects of dapagliflozin, metformin, or exercise on plasma levels of alanine transferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT) in people with prediabetes.
Methods: 120 adults with prediabetes (30-70 years, BMI≥25 kg/m2, HbA1c 5.7-6.4%) were randomized to 13 weeks of dapagliflozin (10 mg once daily), metformin (850 mg twice daily), exercise (30 min, 5 days/week) or control (habitual living). Fasting plasma levels of ALT, AST, and GGT were measured at baseline and after 6 and 13 weeks of intervention and after 26 weeks of follow-up. Linear mixed-effects models were performed.
Results: At baseline, median (Q1;Q3) HbA1c was 5.9% (5.7; 6.1). 17% had elevated ALT levels, 3% elevated AST levels, and 6% elevated GGT levels. ALT and AST levels did not change in response to the interventions (Table 1). Small within-group changes in GGT were observed in the active groups after 6, 13 or 26 weeks, but they did not differ from the control group (P≥0.09 for all comparisons).
Conclusions: 13 weeks of treatment with dapagliflozin, metformin or exercise was not associated with changes in liver function markers compared with habitual living in people with prediabetes.

Disclosure
M.B. Blond: None. K.K. Clemmensen: Research Support; Self; AstraZeneca, Novo Nordisk Foundation. Stock/Shareholder; Spouse/Partner; Novo Nordisk A/S. H. Amadid: None. L.B. Nielsen: None. M. Ried-Larsen: None. K. Karstoft: Employee; Spouse/Partner; Novo Nordisk A/S. F. Persson: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Novo Nordisk A/S. Research Support; Self; Amgen, AstraZeneca, Novo Nordisk A/S. Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Merck Sharp & Dohme Corp., Mundipharma International, Novo Nordisk A/S. M.E. Jørgensen: Research Support; Self; Amgen, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Sanofi-Aventis. Stock/Shareholder; Self; Novo Nordisk A/S. K. Faerch: None.

Funding
Novo Nordisk Foundation; AstraZeneca; University of Copenhagen; Innovation Foundation
873-P: Flash Glucose Monitoring: Effect on Glycaemic Control, Hypoglycaemia, Diabetes-Related Distress, and Resource Utilization: A Nationwide Study
Authors: DESHMUKH HARSHAL , WILMOT EMMA G. , PATMORE JANE , WALTON CHRISTOPHER , HERRING ROSELLE , RYDER ROBERT E. , SATHYAPALAN THOZHUKAT 
Published: June 2020

Aim: The FreeStyle Libre (FSL) flash glucose monitoring device was made available on the UK National Health Services (NHS) drug tariff in 2017. This study aims to explore the UK real-world experience of FSL and the impact on glycaemic control, hypoglycaemia, diabetes-related distress and hospital admissions.
Methods: Clinicians from 102 National Health Service hospitals in the United Kingdom submitted FSL user data, collected during routine clinical care, to a secure web-based tool. T-tests and Mann-Whitney-U tests were used to compare the baseline and follow-up HbA1c and other baseline demographic characteristics. Linear regression analysis was used to identify predictors of change in HbA1c following the use of FSL. Within-person variations of HbA1c calculated adj-HbA1c-SD=SD/sq. Root [n/(n−1)].
Results: Data were available for 10,370 (97% with type 1 diabetes) FSL users; age 38 (±18.7) years, 54% female, diabetes duration 16 (±49) years, and BMI of 25.2 (±16.5) kg/m2. The baseline HbA1c was 69.8 (±18.2) (8.5%), and after a mean follow-up of 7.6 (±23)months (n=3182) HbA1c reduced to 62.3 (±18.5) (7.9%) mmol/l (P&lt;0.0001)). HbA1c reduction was greater in those with initial HbA1c ≥69.5 (&gt;8.5%) mmol/mol, HbA1c reduced from 85.5mmol/mol (±16.07) (10%) to 73.1 mmol/mol (±15.8) (8.8%)) (P&lt;0.0001). The baseline Gold score (score for hypoglycaemic unawareness) was 2.7 (±1.8) and reduced to 2.4 (±1.7) (P&lt;0.0001) at follow-up. 53% of those with a Gold score of ≥4 at baseline, regained hypoglycaemic awareness at follow-up. FSL use was also associated with a reduction in diabetes distress (P&lt;0.0001). Use of FSL was associated with significantly reduced paramedic callouts and hospital admissions due to hypoglycaemia and to hyperglycaemia/Diabetic Ketoacidosis (DKA).
Conclusion: We show that the use of FSL was associated with significantly improved glycaemic control and hypoglycaemia awareness, and a reduction in hospital admissions.

Disclosure
H. Deshmukh: None. E.G. Wilmot: Advisory Panel; Self; Abbott, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic. Speaker’s Bureau; Self; Abbott, Eli Lilly and Company, Insulet Corporation, Novo Nordisk Inc., Sanofi. J. Patmore: None. C. Walton: Speaker’s Bureau; Spouse/Partner; Celgene, LEO Pharma, Novartis Pharmaceuticals Corporation. R. Herring: None. R.E. Ryder: Consultant; Self; GI Dynamics Inc. Other Relationship; Self; Novo Nordisk A/S. T. Sathyapalan: Research Support; Self; Amgen, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Novo Nordisk A/S. Other Relationship; Self; Ipsen Biopharmaceuticals.
877-P: Limits of Correlation Coefficient Analysis in Determining the Minimal Duration of CGM Data Needed to Estimate Time Below Range
Authors: CAMERLINGO NUNZIO , VETTORETTI MARTINA , CIGLER MONIKA , FACCHINETTI ANDREA , SPARACINO GIOVANNI , MADER JULIA K. , CHOUDHARY PRATIK , FAVERO SIMONE DEL ,   ON BEHALF OF THE HYPO-RESOLVE CONSORTIUM
Published: June 2020

To design a clinical trial it is important to know for how long CGM data should be collected to accurately assess time in different glucose ranges (time in ranges). Several studies approached this problem through the computation of the correlation coefficient (R2) between a metric computed in a month-long trial and in several shorter windows of increasing duration. The minimal duration (MD) granting R2&gt;threshold (e.g., 0.9) is then used to estimate the long-term metric.
Here, we focus on time below range (TBR), defined as time &lt;70 mg/dl [%]. We first implemented the R2-based analysis on trials of different duration: A1, A2, A3, lasting 100, 200, 300 days (d), respectively, obtained selecting portions of the same Study A (n=45, duration=360 d, sensor=Abbott Freestyle Libre). Table 1 shows that the longer the trial duration, the larger the resulting MD. Notably, all the obtained trial duration fractions are similar. Then, the analysis was repeated for other two trials of equal duration: Study B (n=85, duration=240 d, sensor=Dexcom G5) and A4, obtained selecting 240 d from Study A. Although B and A4 refer to different T1D populations and different sensors, the resulting trial duration fraction is the same.
In conclusion, the R2-based analysis yields different results based on the duration of the considered dataset, and seems to identify only the fraction of data needed to match the reference TBR.

Disclosure
N. Camerlingo: None. M. Vettoretti: None. M. Cigler: None. A. Facchinetti: None. G. Sparacino: None. J.K. Mader: Advisory Panel; Self; Becton, Dickinson and Company, Eli Lilly and Company, Medtronic, Prediktor Medical, Sanofi-Aventis. Speaker’s Bureau; Self; Abbott, Eli Lilly and Company, Medtronic, Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis. P. Choudhary: Advisory Panel; Self; Abbott, Eli Lilly and Company, Insulet Corporation, Medtronic. Research Support; Self; European Union, JDRF. Speaker’s Bureau; Self; Dexcom, Inc., Novartis AG, Novo Nordisk A/S, Sanofi-Aventis. S. Del Favero: Research Support; Self; Dexcom, Inc.

Funding
Innovative Medicines Initiative 2 Joint Undertaking (777460); European Union; European Federation of Pharmaceutical Industries and Associations; T1D Exchange; JDRF; International Diabetes Federation; The Leona M. and Harry B. Helmsley Charitable Trust
896-P: Putting the Pieces Together: Building a Nonenzymatic Wearable Glucose Sensor Using Silicon Microneedle Arrays
Authors: PENNATHUR SUMITA , KIM HYUNJIN , WANG BING , QUEENAN BRIDGET N. , HUBER DAVID 
Published: June 2020

We developed the technology to build a novel painless CGM that is an on-body, wirelessly transmitting device that extracts interstitial fluid with a dime-sized patch of microneedles and optically detects glucose from extremely small fluid volumes. We not only developed 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), but also demonstrated that our microneedle fabrication is repeatable and reliable. Next we developed an entirely new, stable, non-enzymatic glucose-sensing method based on a variation of the well-known fluorescent-based boronic acid sensor, and further refined this sensor to allow for conductivity based sensing as well as ratiometric sensing of glucose (Figure 1, left). Finally, we built a novel integrated silicon buried dual-junction (BDJ) photodiode-based system with which to detect glucose with our sensor. The Figure attached shows our latest successes with ratiometric sensing of tartaric acid using our microfabricated device (Figure 1, right). Given our results, we believe this technology can now be transitioned to industry, and we are moving on to developing a wearable microfluidic-based patch pump that can allow for a truly affordable artificial pancreas based diabetes management solution.

Disclosure
S. Pennathur: Board Member; Self; Laxmi Therapeutic Devices. Employee; Self; Laxmi Therapeutic Devices. Research Support; Self; American Diabetes Association, Leona M. and Harry B. Helmsley Charitable Trust. Stock/Shareholder; Self; Laxmi Therapeutic Devices. H. Kim: None. B. Wang: None. B.N. Queenan: None. D. Huber: None.

Funding
American Diabetes Association/Pathway to Stop Diabetes (1-17-VSN-18 to S.P.)
9-OR: ADA Presidents’ Select Abstract: Reductions in Management Distress following a Distress Intervention Are Associated with Improved Management and Glycemic Control over Time
Authors: HESSLER DANIELLE M. , FISHER LAWRENCE , MASHARANI UMESH , POLONSKY WILLIAM 
Published: June 2020

Objective: To explore mechanisms by which changes in diabetes distress (DD) can lead to reductions in Hemoglobin A1c among adults with type 1 diabetes over time in the context of a distress intervention.
Methods: In a two-arm randomized trial aimed at reducing DD (a DD-focused vs. an education/management intervention), 301 adults with type 1 diabetes [mean ± SD age was 45.1 ±15.0 years, baseline HbA1c 8.80% ± 1.12]. Individuals were assessed at baseline and at 9 month follow-up on DD, behavioral management, HbA1c and frequency of hypoglycemia (blood glucose &lt; 70 mg/dl). Structural equation modeling tested for significant pathways between changes in DD, behavioral measures, and glycemic control; with separate pathways tested for both the education based and affective focused interventions.
Results: Reductions in management distress were associated with fewer missed insulin boluses, increased problem solving skills, increased diabetes knowledge and more frequent adjustments to insulin regimen in relation to diet (all p &lt; .05). In turn, reductions in missed insulin boluses were linked with improved HbA1c; while increases in problem solving and diabetes knowledge were linked with reductions in frequency of hypoglycemia over a 9-month period. SEM model indices indicated good fit (Chi-square p = .01, CFI = .95, RMSEA = .04). An overall stronger set of linkages were noted for the DD-focused vs. the education/management intervention.
Discussion: In the context of an RCT to reduce DD, results point to significant pathways by which changes in Management Distress operate through changes in disease management and knowledge, which, in turn, drive changes in HbA1C and frequency of hypoglycemic episodes. These findings document the mechanisms through which DD is associated with glycemic control in type 1 diabetes and support the importance of integrating disease management with interventions to reduce DD to maximize improvements in glycemic control.

Disclosure
D.M. Hessler: Consultant; Self; Eli Lilly and Company. L. Fisher: None. U. Masharani: None. W. Polonsky: Advisory Panel; Self; Intarcia Therapeutics, Roche Diabetes Care. Consultant; Self; Abbott, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Novo Nordisk Inc., Onduo, Sanofi US, Xeris Pharmaceuticals, Inc.

Funding
National Institute of Diabetes and Digestive and Kidney Diseases (R01DK094863)
903-P: Hydroxyurea Resulting in False Elevation of Continuous Glucose Sensor Values
Authors: TELLEZ SIOBHAN E. , HORNUNG LINDSEY , COURTER JOSHUA D. , ABU-EL-HAIJA MAISAM , NATHAN JAIMIE D. , LAWSON SARAH , ELDER DEBORAH A. 
Published: June 2020

Objective: To assess the degree, duration, and mean absolute relative difference (MARD) of false values per continuous glucose monitoring (CGM) systems when hydroxyurea (HU) is administered.
Research Design and Methods: Inpatient glucometer and CGM data from 16 post-total pancreatectomy (TPIAT) patients using Dexcom Professional G4 and 6 patients using Dexcom G6 were analyzed after daily dosing with HU. Timing of HU dosing and up to 2 weeks of sensor and glucometer values were assessed in each patient.
Results: A false elevation (&gt;20 mg/dL) of sensor readings were identified after HU dosing. The greatest discrepancy between glucometer and sensor readings occurred 0.5-2 hours after HU administration [G4 (mean 53 mg/dL, median 44 mg/dL, MARD 0.55), G6 (mean 67 mg/dL, median 72.5 mg/dL, MARD 0.79)]. The sensor and glucometer readings were &lt;20 mg/dL mean and median, MARD 0.14 (G4) and &lt;40mg/dL mean and median, MARD 0.18 (G6), by 6 hours after administration. All other prescribed medications were given multiple times each day and did not result in glucose value discrepancy.
Conclusions: There is a false elevation of sensor glucose readings compared to glucometer values (up to 180 mg/dL (G4), 235 mg/dL (G6)) from 0.5hr to 6hr following HU administration. It is important to counsel a patient using a Dexcom CGM system and HU therapy on this finding and to advise reliance on glucometer testing for accurate glucose assessment up to 6 hours after HU administration.

Disclosure
S.E. Tellez: None. L. Hornung: None. J.D. Courter: None. M. Abu-El-Haija: None. J.D. Nathan: None. S. Lawson: None. D.A. Elder: None.
905-P: Is Continuous Glucose Monitoring Effective? Evidence from Real-World Data
Authors: ZHENG YAGUANG , SIMINERIO LINDA M. , ANTON BONNIE , KRALL JODI T. , BEDNARZ LORI L. , LI DAN , NG JASON 
Published: June 2020

Background: Numerous clinical trials have shown the effectiveness of Continuous Glucose Monitoring (CGM) on glycemic control in patients with diabetes; however, real-world evidence of application of CGM is largely lacking. This retrospective study aimed to examine the effects of application of CGM in clinical practice.
Method: Electronic health record (EHR) data from an integrated academic health center’s ambulatory diabetes clinic between May 2014 and May 2019 were used to identify eligible patients diagnosed with diabetes and had used CGM. Diabetes was defined based on ICD-10 Diagnosis Codes. Linear mixed models were used for analysis.
Results: The sample (n=60) was White (86.7%), female (51.7%), with a mean age of 46.9±15.6 years and mean duration of diabetes 19.8±12.4 years. A total of 66.7% of sample had type 1 and 30.0% had type 2 diabetes. The types of CGM were intermittently scanned (51.7%), real-time (36.7%), and passive (11.6%). The HbA1c (%) had a significant reduction after CGM use at 3 months (7.76±1.53) and 6 months (7.76±1.83) compared with 3 months (8.53±1.91) and 6 months (8.62±1.80) before CGM use (p=.002). There were no differences in HbA1c changes by types of CGM (p=.31).
Conclusion: Using EHR data can provide real-world evidence of CGM’s positive impact on glycemic control and on sustained improvement at 6 months. Future research should examine CGM effect gathered from multiple clinics.

Disclosure
Y. Zheng: None. L.M. Siminerio: Research Support; Self; Becton, Dickinson and Company. B. Anton: None. J.T. Krall: Research Support; Self; Becton, Dickinson and Company. L.L. Bednarz: Other Relationship; Self; Medtronic, Tandem Diabetes Care. D. Li: None. J. Ng: None.
91-LB: Study of Glycemic Variability and Hypoglycemia Using Continuous Glucose Monitoring in Type 2 Diabetes Mellitus Changed from Basal-Bolus Therapy to Basal-GLP-1RA Therapy
Authors: MIYOSHI ARINA , OBARA SHINJI , WADA NORIO 
Published: June 2020

Background and Aims: There are currently nine categories of hypoglycemic drugs and the range of choice is expanding. Insulin is very effective but prone to hypoglycemia. Therefore, it is important to consider changing to drugs that are unlikely to cause hypoglycemia in patients who have stable glycemic status. At the same time, however, it should be noted that inappropriate de-escalation of treatment leads to a worsening of the glycemic situation. Thus, in patients with T2D treated with Basal-Bolus Therapy (BBT) using a small amount of bolus insulin and stable glycemic control, under CGM, we had left basal insulin and switched from all bolus insulin to a weekly GLP-1 RA.
Materials and Methods: The subjects were T2D outpatients who were changed from BBT to Basal-GLP-1 RA Therapy (BGT) under CGM during the period from November 2018 to November 2019. Freestyle Libre Pro was used as CGM in all cases. We analyzed average glucose levels, glycemic variability (%CV), Time Below Range (&lt;70mg/dl), Time In Range (70-180 mg/dl) for 3 days before and after treatment change. A total of 18 patients were included in this study. On average, the patient group was aged 58 years and they had been living with T2D for the past 7.0 years. Their HbA1c was 6.6% and BMI was 25.9 kg/m2. Non-fasting CPR was 2.9 ng/ml. Total daily bolus insulin dose was 12.6 units.
Results: In all cases, bolus insulin was changed to dulaglutide 0.75mg with the same amount of basal insulin. There was no deterioration in mean glucose levels, TBR and TIR (115.4 vs. 105.7 mg/dl, 8.2 vs. 7.5%, 81.4 vs. 87.6%, respectively). %CV (21.1 vs. 15.7%; P = 0.029) and HbA1c (6.6 vs. 6.3%; P = 0.020) were significantly improved.
Conclusion: In T2D with stable glycemic status and maintained endogenous insulin secretion, stepping down from BBT using a small amount of bolus insulin to BGT was able to better manage glycemic level and variability under CGM observation.

Disclosure
A. Miyoshi: None. S. Obara: None. N. Wada: None.
919-P: Comparison of Glycated Haemoglobin with Glycated Albumin as Glycemic Marker in Patients with Iron Deficiency Anaemia: Real-World Evidence from Indian Setting
Authors: THACKER HEMANT , NITEY PRATEEK , MAKKAR BRIJ M. , MAHESHWARI ANUJ 
Published: June 2020

Strict glycaemic management is cornerstone of metabolic control. We prospectively compared Glycated Albumin (GA) with Glycated Hemoglobin (HbA1c) for the preciseness as marker for glycemic indicator in T2DM patients presenting with iron deficiency anaemia (n=45). Patients were treated with IV iron therapy (Iron Isomaltoside) without changing the metabolic treatment protocol. Table summarises the progressive change over baseline, two and four weeks in Hemoglobin (Hb), FPG, HbA1c and GA. 25 patients (56%) were females; mean age was 52.1 ± 10.74 years. Hb was negatively correlated with HbA1c (r = - 0.001; p = 0.99 NS) and positively corelated with GA (r = 0.29; p = 0.44 NS). Mean (±SD) percentage difference in the target and achieved values of HbA1c (6.5%) and GA (&lt;18%) was 6 (±3.6) and -19 (±10), respectively. All the patients achieved glycemic targets when assessed with GA in contrast to none achieving HbA1c targets. The (GA/HbA1c) ratio did not ranged widely (range 0.75), mean 2.1 (±0.27, 95% CI 1.9 to 2.3). HbA1c values may be misleading, whereas GA, which is independent of anemia would be a precise monthly glycemic indicator than HbA1c to evaluate the glycemic control and fluctuations, in patients with anaemia to achieve strict and early glycemic control and further optimise the treatment. The results need corroboration with larger studies.

Disclosure
H. Thacker: None. P. Nitey: None. B.M. Makkar: Advisory Panel; Self; Abbott, AstraZeneca, Biocon, Boehringer Ingelheim Pharmaceuticals, Inc., Eris Lifesciences, Novartis AG, Novo Nordisk Inc., Sanofi, USV Private Limited. Speaker’s Bureau; Self; Janssen Pharmaceuticals, Inc. A. Maheshwari: None.