bluuebunny/crossref_metadata_2025 huggingface dataset:
824-P: Quality of Behavior Change Action Plans Created in Primary Care Settings as a Tool for Patient Involvement in Type 2 Diabetes Care
Authors: KJAER PERNILLE H. , DEDHIA MANSI , PARRA JOSÉ , FISHER LAWRENCE , POTTER MICHAEL B. , EJSKJAER NIELS , SKOVLUND SOREN E. , HESSLER DANIELLE M.
Published: June 2020

Objectives: Action plans based on patient reported outcomes (PROs) made in consultation by adults with type 2 diabetes (T2D) and their health care team have been proposed as a tool to improve clinical outcomes. The aim is to evaluate the quality of action plans.

Methods: A two-arm cluster-randomized trial comparing Connection to Health (CTH) and Enhanced Engagement CTH (EE-CTH) in 12 primary care sites in the San Francisco Bay Area. Adults with T2D filled out a health survey prior to consultation to collect PROs. Action plans made in consultation focused on unhealthy behavior detected by the survey. Providers in EE-CTH were additionally trained in motivational interviewing and relationship building skills. Action plan quality was rated using an adapted version of the GET-D coding system (dual coding of 20%, IRR >80%), and examined for associations with intervention, patient demographics and PROs.

Results: Overall quality of action plans was moderate-high with a mean score of 14.22 (SD=4.07) on a 0-20 quality scale (n=700). Significantly higher quality plans were created in CTH (M=14.87, SD=3.92) compared to EE-CTH (M=13.50, SD=4.12) (t(698)=4.50, p<.001). Correlations with PROs and patient demographics were calculated with Pearson’s (r) or Spearman’s (rs) correlation. Higher patient health literacy and confidence in carrying out the plan were positively related to quality of action plans (rs =.089, p=.018) (rs =.138, p<.001), whereas gender, age, language, race, level of education, depression, and social determinants of health (SDH) were unrelated to action plan quality (p=n.s.).

Discussion: Both CTH and EE-CTH resulted in moderate-high quality action plans, but considerable quality variability was seen across patients. Action plan quality was significantly higher in CTH compared to EE-CTH. Results suggest no link to most patient demographics and PROs. However, health literacy and confidence level are positively related to action plan quality.

Disclosure

P.H. Kjaer: None. M. Dedhia: None. J. Parra: Employee; Self; Behavioral Diabetes Institute. L. Fisher: None. M.B. Potter: None. N. Ejskjaer: None. S.E. Skovlund: None. D.M. Hessler: Consultant; Self; Eli Lilly and Company.

Funding

National Institutes of Health (DK108039)
832-P: Resilience Outstrips the Negative Impact of Diabetes Distress on Glycemic Control and Self-Management among Youth with Type 1 Diabetes
Authors: LUO DAN , XU JINGJING , ZHU MIN , WANG HONG , WANG YUBING , SHI YUN , ZHANG MEI
Published: June 2020

Background: Adolescent with type 1 diabetes (T1D) experience increased diabetes distress, which is linked to poor self-management and glycemic control. Resilience might be able to decrease distress and facilitate disease management.

Objective: To determine whether resilience buffers the deleterious consequences of distress on glycemic control and self-management.

Methods: A convenience sample of 203 eligible adolescents (mean age 14.60 years) was recruited from a national endocrine center. Data were collected on social clinical characteristics, diabetes distress, resilience, self-management and glycemic. We preformed the simple slope and Johnson-Neyman statistical technique to probe moderating effects of resilience.

Results: A total of 42.4% of adolescents reported high diabetes distress. Our results indicated that resilience moderated the association that hazards of severe diabetes distress for higher HbA1c (β = 0.148, t = 4.156, p = 0.000) and worse self-management (β = -0.010, t = -3.000, p = 0.003) were only apparent in the context of low resilience.

Conclusions: We highlight the interactive relationship between diabetes distress and resilience in relation to glycemic control and self-management. It suggest that resilience is promising intervention target for distressed T1D adolescents with unsatisfactory glycemic control and self-management behavior.

Disclosure

D. Luo: None. J. Xu: None. M. Zhu: None. H. Wang: None. Y. Wang: None. Y. Shi: None. M. Zhang: None.
840-P: An Internet-Based Interventional Study in a Real-World Setting Improves Metabolic Control in Patients with Hyperglycaemia
Authors: ZHANG LEI , CHEN XIAORONG , DONG CHAO , WANG HAIYAN , WU NING , YANG YUXIU
Published: June 2020

Objective: Strictly designed clinical trials have shown benifit of lifestyle Intervention on the prevention of type 2 diabetes (T2DM). Translational data in real-world setting, however, is still limited.

Methods: In December 2015, a Multi-Disciplinary Team (MDT), consisting of endocrinologist, dietitian, certified exercise instructor, psychotherapist and clinical nurses was built to conduct comprehensive life-style intervention on 65 volunteers with hyperglycaemia (43 with T2DM and 22 with prediabetes) based on a WeChat group. At baseline, the patients had mean age of 48.2±4.1 yrs. Each patient was requested to upload pictures of daily meals, FPG and 2hPG, and exercise logs recorded by wearable device and the MDT experts would comment in the group. In addition, a half-day event was held at enrollment, week 4, and week 8, in which patients received face to face instructions, as well as measurements of body weight, FPG, 2hPG, HbA1c, serum triglyceride (TG) and total cholesterol (TC). Data before and after the intervention were compared by using paired T-test.

Results: A total of 59 patients accomplished intervention. The follow-up rate was 90.7%. At week 8, BMI (26.06 vs. 24.91kg/m2), FPG (8.56 vs. 6.7mmol/L), 2hPG (17.54 vs. 12.75mmol/L), HbA1c (7.28 vs. 6.53%), and TC (5.12 vs. 4.60mmol/L) were significantly decreased (all p-values < 0.01) among T2DM subgroup. Similar Figure was seen for BMI (27.86 vs. 26.57Kg/m2), FPG (6.21 vs. 5.89mmol/L), 2hPG (9.04 vs. 8.10mmol/L), HbA1c (5.98 vs. 5.78%), and TC (5.43 vs. 4.74mmol/L) among prediabetes subgroup. TG was also decreased in both subgroups but the difference did not reach statistical significance. The proportion of patients who simultaneously achieved goals of HbA1c (<7%), BMI (<26.0 Kg/m2) and TC (<4.50mmol/L) increased 16.3%.

Conclusion: In a real-world setting, well designed online plus offline intervention approach improves metabolic control in patients with hyperglyceamia.

Disclosure

L. Zhang: None. X. Chen: None. C. Dong: None. H. Wang: None. N. Wu: None. Y. Yang: None.
841-P: Predictive Ability of Incident Type 2 Diabetes Mellitus (T2DM) Using Machine Learning Algorithms: A Meta-analysis
Authors: KODAMA SATORU , SATO TAKAAKI , YAMAMOTO MASAHIKO , ISHIGURO HAJIME , IWANAGA MIDORI , FUJIHARA KAZUYA , YAMADA TAKAHO , KATO KIMINORI , SONE HIROHITO
Published: June 2020

Machine learning (ML) methods receive increasing attention for diabetes risk assessment. This meta-analysis quantitatively assessed the predictive ability of ML algorithms for incident T2DM. Electronic literature searches were done using MEDLINE and EMBASE for longitudinal studies whereby an index test was a model constructed by ML algorithms for predicting incident T2DM. Reference standard was a blood test or physicians’ diagnosis confirming T2DM. The model was restricted to algorithms that required the aid of ML (e.g., neural network, support vector machine, etc.). The 2x2 contingency data (i.e., true-positive, false-positive, false-negative, and true-negative) were pooled with a hierarchical summary receiver operating characteristic model. Pooled sensitivity and specificity (95% confidence interval (CI)) of 8 eligible studies were 0.70 (0.66-0.74) and 0.78 (0.69-0.85), respectively. (Figure) Positive likelihood ratio (PLR) calculated as sensitivity/(1-specificity) and negative likelihood ratio (NLR) calculated as (1-sensitivity)/specificity were 3.25 (95%CI, 2.25-4.58) and 0.38 (95% CI, 0.34-0.43), respectively. We conclude that existing prediction models are not yet satisfactory considering that PLR>5 moderately increases the probability of a disease given a positive test and NLR<0.2 moderately decreases the probability of a disease given a negative test.

Disclosure

S. Kodama: None. T. Sato: None. M. Yamamoto: None. H. Ishiguro: None. M. Iwanaga: None. K. Fujihara: None. T. Yamada: None. K. Kato: None. H. Sone: Research Support; Self; Kyowa Hakko Kirin Co., Ltd., Novartis AG, Ono Pharmaceutical Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co.
846-P: Improvement in Glucose Metabolism with Four Weeks of Neuromuscular Electrical Stimulation in Sedentary Adults
Authors: BAJPEYI SUDIP , GALVAN MICHELLE , SANCHEZ MICHAEL J. , BOYLE JASON B. , MIN KISUK , AGULLO FRANCISCO J. , COVINGTON JEFFREY D.
Published: June 2020

Most U.S. adults do not meet minimum exercise recommendations by ACSM. We and others have shown an increase in GLUT4 protein and glucose uptake with electrical stimulation induced muscle contraction using an in vitro cell culture model. However, effectiveness of neuromuscular electrical stimulation (NMES) in humans to improve insulin sensitivity is not clear.

Purpose: To determine the effects of four weeks of NMES on insulin sensitivity, substrate utilization, and muscle mass in sedentary humans.

Methods: Sedentary overweight/obese subjects (n=10; Age 36.8 ±3.8 yrs; BMI 32.0 ±1.3 kg/m2) were randomized into a control (Con) or NMES group. All participants received bilateral quadriceps stimulation (3x/wk; 30 min/ses) either at low intensity sensory level (Con) or high intensity neuromuscular level (NMES) for 4 weeks (50Hz). Insulin sensitivity was assessed by oral glucose tolerance test (OGTT), substrate utilization by indirect calorimetry, blood lactate by lactate analyzer, and body composition by DXA.

Results: Con and NMES groups had comparable fasting blood glucose, glucose tolerance, substrate utilization, and muscle mass (all p>0.05) at baseline. Four weeks of NMES tended to improve glucose tolerance, measured by glucose area under the curve (AUC) (455.6 ±26.1 to 415.4 ±25.9 AU; p=0.07) whereas no change was observed in Con (430.7 ±20.2 to 494.7 ±77.2 AU; p=0.76). Glucose level after 2 hours of glucose consumption during OGTT was significantly lower after 4 weeks of NMES (150.9 ±7.6 to 138.2 ±7.6 mg/dL; p=0.03) whereas no change was observed in Con (151.8 ±6.1 to 173.2 ±34.2 mg/dL; p=0.33).Additionally, lactate AUC assessed during 30 min of NMES, was significantly greater compared to that of Con (p<0.02). There was no change in substrate utilization (p=0.85) or muscle mass (p=0.17) after NMES intervention.

Conclusion: NMES is a novel and effective strategy to improve insulin sensitivity and glucose utilization in an at-risk overweight/obese sedentary population.

Disclosure

S. Bajpeyi: None. M. Galvan: None. M.J. Sanchez: None. J.B. Boyle: None. K. Min: None. F.J. Agullo: None. J.D. Covington: None.

Funding

University of Texas at El Paso
848-P: Totum-63 Lowers Fasting Glycemia in Subjects with Prediabetes: A Phase 2A Clinical Trial
Authors: PELTIER SEBASTIEN , CHAVANELLE VIVIEN , OTERO YOLANDA F. , BARGETTO MAXIME , CAZAUBIEL MURIELLE , PEREIRA BRUNO , SIRVENT PASCAL , BARD JEAN-MARIE
Published: June 2020

Over 1 billion people worldwide suffer prediabetes, which is defined by increased fasting blood glucose (FBG), glucose intolerance and/or higher A1c hemoglobin. Prediabetes is considered a risk factor for type 2 diabetes (T2D). We have developed Totum-63, a plant-based product designed to reduce T2D risk factors. In a phase I/II clinical trial, Totum-63 had shown its safety, good tolerance and beneficial effects on post-prandial glucose control in healthy subjects. The aim of this multicentre, randomised and double-blind placebo-controlled phase IIA trial was to show the efficiency of Totum-63 in reducing FBG (primary endpoint) in 51 volunteers with prediabetes (defined by FBG ≥ 1.1 g/l and 2h OGTT glycemia ≥ 1.1 g/l). Secondary endpoints included blood glucose change following an oral glucose tolerance test (OGTT) and anthropometric parameters. Totum-63 (5g/day) or placebo was given to the participants for 6 months.

At the end of the supplementation period, FBG was reduced in Totum-63 group compared to placebo group (difference from baseline: -0.04 ± 0.02 vs. +0.09 ± 0.04 g/l, p<0.05). Similarly, 2-hour OGTT glycemia was improved in Totum-63 group, vs. placebo (difference from pre-protocol value: -0.02 ± 0.07 vs. +0.32 ± 0.17 g/l, p<0.05). Moreover, Totum-63 had a significant lowering effect on body weight (-0.07 ± 0.42 vs. +1.83 ± 0.57 Kg; p<0.05) and waist circumference (-1.67 ± 0.73 vs. +2.81 ± 0.65 cm; p<0.001).

To conclude, the primary endpoint of this clinical trial was reached, Totum-63 contributed to lower FBG in individuals with prediabetes. Moreover, the response to an OGTT was improved, as well as body weight and waist circumference, thus making Totum-63 a promising candidate for T2D risk prevention.

Disclosure

S. Peltier: Board Member; Self; Valbiotis. V. Chavanelle: Employee; Self; Valbiotis. Y.F. Otero: Employee; Self; Valbiotis. M. Bargetto: Employee; Self; Valbiotis. M. Cazaubiel: Board Member; Self; Valbiotis. B. Pereira: None. P. Sirvent: Board Member; Self; Valbiotis. J. Bard: Consultant; Self; Biofortis, Valbiotis. Stock/Shareholder; Self; Valbiotis.
863-P: In Patients without Diabetes, Glycemic Variability Derived from Continuous Glucose Monitoring (CGM) Data Relates to Hyperglycemia More Than Insulin Resistance
Authors: ZHANG YUAN , OLAWSKY EVAN A. , ALVEAR ALISON C. , EBERLY LYNN E. , CHOW LISA S.
Published: June 2020

In adults without diabetes, the relationship between CGM derived glycemic variability and glycemic measures is unknown. We hypothesized that greater dysglycemia will be associated with greater 2 week glycemic variability.

Methods: Subjects without self-reported diabetes [n=22, 19F/3M, mean (SD): 44.8 (12.1) years, 34.4 (7.4) kg/m2, HbA1c: 5.5% (0.4) underwent 2 hr OGTT followed by 14 days of CGM (FreestyleLibrePro). Subjects were classified as either normal or dysglycemic using several glycemic measures [based on HbA1c, disposition index, HOMA-IR, Matsuda (Table1)]. Multiple assessments of glycemic variability were derived from CGM data (Table1).

Results: Across all subjects, HbA1c significantly correlated with mean glucose, J-index, High blood glucose index and Area under the curve (r=0.4-0.5, p<0.05). HbA1c negatively correlated with Time in Range (70-99 mg/dl, r=-0.51, p<0.05). Glycemic variability measures did not correlate with HOMA-IR, Matsuda, or disposition index. Similar results were found when comparing glycemic variability measures between dysglycemia groups.

Conclusion: In patients without diabetes, greater glycemic variability was associated with greater Hba1c. These findings support hyperglycemia rather than insulin resistance as the primary driver in glycemic variability in this population.

Disclosure

Y. Zhang: None. E.A. Olawsky: None. A.C. Alvear: None. L.E. Eberly: None. L.S. Chow: None.

Funding

National Institutes of Health (UL1TR002494); Healthy Foods Healthy Lives Institute (17SFR-2YR50LC)
87-LB: Glycemic Variability in People with Impaired Glucose Tolerance/Prediabetes
Authors: DOUGLAS KEVIN , ANNAMALAI NARAYAN , MOORE PATRICK J. , THOMSON SCOTT
Published: June 2020

The use of continuous glucose monitoring has been suggested as a method of determining glucose control in prediabetes, including understanding glucose variability, which may be a risk factor in the progression to type 2 diabetes. This pilot research study aimed to determine glycemic variability (%CV) in people diagnosed with impaired glucose tolerance (IGT)/prediabetes. The study included people aged 18 or over, with their most recent HbA1c 5.7-6.4% (39-47 mmol/mol) recorded in medical notes in the last 12 months. Pregnant patients were excluded, as were patients with diabetes. A total of 43 participants (60.5% female) from 5 primary care sites in the UK enrolled in the 2-week single arm study, wearing a FreeStyle Libre Pro Flash Glucose Monitoring SystemTM (glucose data was not available to participants). On average, HbA1c was 6.06±0.25% (42.7±2.6 mmol/mol), age was 62.5±8.3 years, BMI was 32.1±6.6 kg/m2, average time since diagnosis was 18±19 months, average Q diabetes score was 28.4±23.7% (mean±SD). Glucose variability (%CV) was 17.3±3.8%, %CV was greater during daytime hours (06:00 to 23:00) than at night (23:00 to 06:00), 17.7±4.1% and 12.9±3.5% respectively (mean±SD). %CV was greater in those with BMI<30 kg/m2 compared to those ≥30 kg/m2; 18.9±3.3% and 15.8±3.5% respectively (mean±SD). Mean glucose was 101.2±10.0 mg/dL, time in hyperglycemia (>180 mg/dL, 10.0 mmol/L) was 0.09±0.23 hours per day (mean±SD). Ten anticipated sensor insertion site symptoms were experienced by five participants: erythema (n=2, well-defined redness), pain (n=1), bruising (n=1), itching (n=2), rash (n=2), bleeding (n=1) and other (n=1, ‘skin irritation’), all were mild in severity and resolved. This population, with prediabetes experienced glucose variability of 17.3% (%CV). This was greater during daytime hours and in those with lower BMI (<30 kg/m2).

Disclosure

K. Douglas: None. N. Annamalai: None. P.J. Moore: None. S. Thomson: Research Support; Self; Abbott. Research Support; Spouse/Partner; Abbott.
87-OR: NF Kappa B Expression Is Related to Diabetes and Impaired Respiratory Capacity of Human Ventricular Myocardium
Authors: ZWECK ELRIC , SCHEIBER DANIEL , JELENIK TOMAS , HORN PATRICK , PESTA DOMINIK , LASSNER DIRK , SCHULTHEISS HEINZ-PETER , BOEKEN UDO , AKHYARI PAYAM , LICHTENBERG ARTUR , KELM MALTE , RODEN MICHAEL , WESTENFELD RALF , SZENDROEDI JULIA
Published: June 2020

Nuclear factor kappa-B (NFkB) has been postulated to induce cardiac fibrosis and dysfunction in heart failure and triggers inflammatory pathways. NFkB can be induced by damaged mitochondria. Its association with myocardial mitochondrial respiratory function in non-ischemic diabetes-related heart failure in humans is yet unclear. We hypothesized that human ventricular myocardial NFkB expression (i) is increased by type 2 diabetes mellitus (T2DM), and (ii) relates to reduced myocardial mitochondrial respiration. Heart transplant recipients with or without T2DM (as determined by oral glucose tolerance test), about to undergo post-transplant surveillance endomyocardial biopsies, were included, if they had received hearts from donors without T2DM. Thus, time since transplantation equaled diabetes-exposure of the transplanted hearts (2.9±2.4 years). We assessed normalized NFkB p105 subunit (NFkB1) mRNA expression using real-time PCR and myocardial mitochondrial respiration using high-resolution respirometry. Study participants (T2DM: n=17, Non-DM: n=32) had no histological signs of allograft rejection, heart failure (left ventricular ejection fraction 66.2±6.5%) or coronary artery disease. Age and sex distribution were similar between Non-DM and T2DM (p=0.50; p=0.40, respectively). Myocardial NFkB1 expression was 60% higher in T2DM than in Non-DM (0.28 [0.21-0.44] vs. 0.45 [0.29-0.71] arbitrary units, p<0.05). Fasting blood glucose, but not fasting free fatty acids (p=0.55), related to myocardial NFkB1 mRNA expression (r=0.31; p<0.05). NFkB1 expression related inversely to state 3 respiration of fatty acids and glutamate (r=-0.33; p<0.05), suggesting impaired complex I-related respiration. Our findings support the concept of a key role of NFkB in diabetes-related heart failure via altered myocardial respiratory function in humans. NFkB1 might therefore serve as a future target for treating diabetes-related heart disease.

Disclosure

E. Zweck: None. D. Scheiber: None. T. Jelenik: Employee; Self; Boehringer Ingelheim Pharmaceuticals, Inc. P. Horn: None. D. Pesta: None. D. Lassner: None. H. Schultheiss: None. U. Boeken: None. P. AKhyari: None. A. Lichtenberg: None. M. Kelm: None. M. Roden: Advisory Panel; Self; Servier. Board Member; Self; Poxel SA. Consultant; Self; Eli Lilly and Company, Gilead Sciences, Inc., ProSciento, TARGET PharmaSolutions. Research Support; Self; Boehringer Ingelheim International GmbH, Novartis Pharma K.K., Sanofi US. Speaker’s Bureau; Self; Novo Nordisk A/S. R. Westenfeld: None. J. Szendroedi: None.

Funding

German Research Council (SFB1116); Heinrich-Heine University
872-P: Improved HbA1c Estimation Using CGM Data
Authors: GROSSMAN JOSHUA , WARD ANDREW T. , MAAHS DAVID M. , PRAHALAD PRIYA , SCHEINKER DAVID
Published: June 2020

The Glucose Management Indicator (GMI) is the accepted method to estimate HbA1c from mean CGM glucose. We hypothesize that HbA1c estimation could be improved with machine learning (ML) methods that account for additional covariates.

CGM, HbA1c, and demographic data were aggregated from five studies (3,550 HbA1c measurements, 1,095 participants). For each HbA1c, the following statistics were derived from all CGM data recorded up to 30 days before and 5 days after measuring HbA1c: mean, SD, CV, and % time in various ranges. Using HbA1c as the outcome and all CGM glucose statistics and demographics as covariates, the ML models LASSO, LASSO with all two-way interactions, and random forest were tuned via 5-fold cross validation (CV). Top performing models were compared to GMI via 5-fold CV and the paired Wilcoxon signed-rank test.

Random forest performed best, with 20% lower estimation error than GMI (p < 0.001). After restricting HbA1c measurements to those for which an additional HbA1c had been measured for the same patient at least 70 days prior (1,833 HbA1c measurements, 755 participants), the models were re-trained with prior HbA1c as a covariate. LASSO performed best, with 27% lower estimation error than GMI (p < 0.001). An ordinary least squares (OLS) model accounting for mean glucose, race, and prior HbA1c had an estimation error only 0.3% higher than LASSO, suggesting that augmenting GMI with just two additional covariates could substantially improve estimation.

Disclosure

J. Grossman: None. A.T. Ward: None. D.M. Maahs: Advisory Panel; Self; Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk A/S. Consultant; Self; Abbott, Sanofi. Research Support; Self; Bigfoot Biomedical, Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care. P. Prahalad: None. D. Scheinker: None.
876-P: Accuracy of Continuous Glucose Monitoring Compared with Capillary Blood and Venous Plasma Glucose Measurements in Medical Intensive Care Unit Patients
Authors: TINGSARAT WANNITA , LAICHUTHAI NITCHAKARN , BOONCHAYA-ANANT PATCHAYA , BURANASUPKAJORN PATINUT , KHOVIDHUNKIT WEERAPAN
Published: June 2020

Objective: To assess the accuracy of the subcutaneous continuous glucose monitoring (CGM) sensor by comparing to the capillary blood glucose (CBG) and venous plasma glucose (VPG) measurements in the medical intensive care unit (MICU) patients.

Subjects and Methods: In a prospective study in patients in the MICUs, Medtronic Enlite sensor was inserted in the abdominal area. Paired sensor glucose readings with reference glucose values (CBG and VPG) were collected from MICU patients receiving intravenous insulin infusion therapy. The accuracy was assessed using the mean absolute relative difference (MARD), surveillance error grid (SEG) analysis and modified Bland-Altman plot.

Results: Twelve patients completed the study (age 69.3 ± 11.6 years; BMI 21.6 ± 2.9 kg/m2; APACHE II score 21.8 ± 6.3 and duration of CGM use 108.7 ± 41.1 hours). A total of 353 paired CGM and CBG glucose readings and 125 paired CGM and VPG readings were included in the analysis. Using CBG as the reference, MARD was 7.2%. The modified Bland-Altman plot showed 95% of the limit of agreement (LoA) was -18.4% to 19.2%. The SEG analysis showed that 100% of paired glucose values were within zones A or B. No clinically significant difference in the accuracy was seen between subgroups of vasopressor use (MARD 6.1% in the vasopressor group vs. 6.8% in the non-vasopressor group, p = NS). Using VPG as the reference, the MARD was 8.8%. The modified Bland-Altman plot showed 95% LoA of - 22.6% to 28.2%. The SEG analysis showed 95.2% of glucose readings were within zones A or B.

Conclusion: The accuracy of the subcutaneous CGM sensor in MICU patients was comparable to that observed in non-critical care settings, with no device-related adverse events. The sensor accuracy remained within the similar range when using VPG as the references compared to using CBG as the references. No differences in terms of accuracy between the vasopressor and non-vasopressor groups were found in this study.

Disclosure

N. Laichuthai: None. P. Buranasupkajorn: None. W. Khovidhunkit: Speaker’s Bureau; Self; Amgen, AstraZeneca.

Funding

Quality Improvement Fund; King Chulalongkorn Memorial Hospital; Thai Red Cross Society (1-62-30101-A-11)
878-P: Real-World Data from U.S. Patients Using a Long-Term Implantable Continuous Glucose Monitoring (CGM) System: Age Effect on Glycemic Control
Authors: TWEDEN KATHERINE , GHOSH-DASTIDAR SAMANWOY , DEHENNIS ANDREW D. , KAUFMAN FRANCINE
Published: June 2020

Background: Data from the first ∼200 adult real-world U.S. users of the implantable 90-day fluorescence-based glucose sensor, Eversense CGM System, showed 62.3% time in range (TIR, glucose 70-180mg/dL) and 4.1% time <70mg/dL. This report describes CGM data by age in a large cohort (>1600) of users with Eversense.

Methods: Sensor glucose (SG) data were analyzed for users who reached a 90-day wear period. Mean SG, variability measures (SD, CV), glucose management indicator (GMI), and CGM metrics for 18-24.9, 25-44.9, 45-64.9, ≥65 years of age subgroups were computed. Transmitter wear time was calculated in users with at least 30 wear days.

Results: We evaluated 1656 users (997 with T1D, 261 with T2D, 408 unreported). Percent time in range (TIR), time below ranges (<70 mg/dL and <54 mg/dL), and time above ranges (>180 mg/dL and >250 mg/dL) improved with age, with 52.6% and 69.0% TIR in the youngest and oldest groups, respectively (Table). Transmitter wear time was 82% overall and improved to 87% in those ≥65 years. Percent users achieving goals for targets also improved with age.

Conclusion: Glycemic outcomes improved with age in a large cohort using the implantable Eversense CGM System. Younger adults had favorable outcomes compared to data from the T1D Exchange. Overall, these data suggest that the implanted Eversense CGM system is a valuable tool for management of diabetes.

Disclosure

K. Tweden: Employee; Self; Senseonics. S. Ghosh-Dastidar: Employee; Self; Senseonics. A.D. Dehennis: Employee; Self; Senseonics. F. Kaufman: Advisory Panel; Self; Virta Health Corp. Employee; Self; Senseonics. Stock/Shareholder; Self; Medtronic.
881-P: Remission through Early Monitored Insulin Therapy: Duration Month
Authors: SOBEL SANDRA I. , RUPPERT KRISTINE , MCCARTHY PATRICK M. , SIMINERIO LINDA M. , ROMETO DAVID A.
Published: June 2020

Background: Studies show titrating insulin in the hospital for 2-4 weeks at diagnosis of type 2 diabetes mellitus (T2DM) can restore production of endogenous insulin to achieve remission. The purpose of the Remission through Early Monitored Insulin Therapy- Duration Month (REMIT-DM) pilot study was to evaluate if individuals with T2DM in an outpatient setting could self-titrate insulin with an innovative titration algorithm using glucose values and trends from a real-time continuous glucose monitor (CGM).

Methods: A diabetes educator trained 10 insulin naïve T2DM participants on use of a basal-bolus multiple daily insulin injection algorithm designed to achieve euglycemia (fasting glucose (FG) < 100 mg/dL, 2-hour (2h) post-prandial glucose (PPG) < 120 mg/dL) during 4 weeks of insulin therapy based on Dexcom G6 real-time CGM values and trends. Primary outcome was participant ability to do algorithm titrations using CGM readings. Secondary outcomes included achievement of goal FG and 2h-PPG, time in days to achievement of goals, CGM time in range (TIR), patient distress via the PAID-5 scale prior to and at completion of the 4-week intervention, and assessment of satisfaction with REMIT-DM experience.

Results: Nine of 10 participants were able to use the self-titration algorithm while 7 achieved goal FG and 8 goal 2h-PPG over 4 weeks of insulin therapy. Mean time to FG and 2h-PPG goals were 9.75 ± 6.01 and 7.11 ± 4.64 days, respectively. Mean CGM TIR during insulin therapy was 80.67 ± 10.7%. Mean PAID-5 scores pre-post use of titration protocol was 8.9 and 7.6, respectively. Scores >8 indicate possible diabetes-related distress. All reported high satisfaction with experience.

Conclusions: Insulin naïve patients were able to follow and self-titrate insulin using real-time CGM values and trends according to an innovative remission insulin algorithm in an outpatient setting. DM-related distress was reduced while all reported satisfaction. Insulin titration at diagnosis warrants further exploration.

Disclosure

S.I. Sobel: None. K. Ruppert: None. P.M. McCarthy: None. L.M. Siminerio: Research Support; Self; Becton, Dickinson and Company. D.A. Rometo: None.

Funding

University of Pittsburgh Clinical and Translational Science Institute
883-P: Utility of CGM Technology in the Inpatient Setting
Authors: ALOI JOSEPH A. , PRICE CATHERINE E.
Published: June 2020

Effective inpatient insulin management is an important safety issue for our patients. Barriers to appropriate insulin use include incorrect timing of prandial insulin administration, failure to administer basal insulin to persons with type 1 DM, and insulin dosage overtreatment resulting in severe hypoglycemia. We studied the use of Abbott professional (blinded) Libre sensor in patients either on basal bolus insulin therapy (n=20) or on IV insulin (n=20) as a potential aide in minimizing insulin errors. The Wake Forest Baptist Health IRB approved the study and all subjects studied signed informed consent. Continuous BG data were aggregated for each subject and compared to point of care blood glucose (BG), iStat, or lab chemistry values. BG results were assessed for comparability (CGM versus Standard BG testing) and in ability to detect hypoglycemia early in those subjects experiencing hypoglycemia.

Analysis of the basal bolus insulin arm revealed that an average difference of standard of care versus CGM glucose reading of 44.80 mg/dL. Differences in basal bolus arm BG comparing 24-hour average CGM glucometric data to standard of care was similar at 43.56 mg/dL. One third of participants in the basal bolus arm had at least one episode of hypoglycemia (BG < 70 mg/dL.) The average BG value in those experiencing a hypoglycemic event in our study group was 59 mg/dL on Standard BG testing. In retrospective review, all episodes of hypoglycemia were detected earlier on CGM technology. In one occurrence, hypoglycemia noted 6 hours sooner on CGM. CGM data consistently shows lower BG values than those using standard methods. Additional data including difference in insulin dosing based on CGM vs. Standard BG testing following completion of the IV insulin arm will be discussed.

Disclosure

J.A. Aloi: Consultant; Self; Medtronic. Research Support; Self; Medtronic. C.E. Price: None.

Funding

Wake Forest Baptist Medical Center
886-P: Saliva 1,5-Anhydroglucitol Detected by Liquid Chromatography-Mass Spectrometry Is a New Marker for Diabetes Screening
Authors: BAO YUQIAN , JIAN CHAOHUI , ZHAO AIHUA , MA XIAOJING , GE KUN , LU WEI , ZHU WEI , WANG YUFEI , ZHOU JIAN , JIA WEI
Published: June 2020

Context: Unlike other commonly used invasive blood glucose monitoring methods, saliva detection prevents patients from suffering the physical uneasiness. Serum 1,5-anhydroglucitol (1,5-AG) is a new indicator for assessing short-term glycemic control. However, there are currently few studies on saliva 1,5-AG in diabetes mellitus (DM).

Objective: This study aimed to evaluate the effectiveness of saliva 1,5-AG in diabetes screening in a Chinese population.

Design and Participants: A total of 641 subjects without a valid diabetic history (251 men and 390 women) were recruited from September 2018 to June 2019 and underwent an oral glucose tolerance test (OGTT). Fasting saliva 1,5-AG (1,5-AG0) and postload 2-hour saliva 1,5-AG (1,5-AG120) were measured with liquid chromatography-mass spectrometry. Serum 1,5-AG0 and 1,5-AG120 were measured with enzymatic method.

Results: Saliva 1,5-AG levels in DM subjects were lower than those in nondiabetes mellitus subjects (both P < 0.05). Saliva 1,5-AG was positively correlated with serum 1,5-AG and negatively correlated with blood glucose and glycated hemoglobin (HbA1c) (all P < 0.05). The optimal cutoff points of saliva 1,5-AG0 and 1,5-AG120 for diabetes screening were 0.436 μg/mL and 0.438 μg/mL, respectively. Fasting plasma glucose (FPG) combined with saliva 1,5-AG0 and HbA1c combined with saliva 1,5-AG120 reduced the proportion of people who required an OGTT by 47.22% and 51.41% compared with FPG alone and HbA1c combined with FPG, respectively.

Conclusion: The optimal cutoff point of saliva 1,5-AG for diabetes screening was 0.44 μg/mL. Saliva 1,5-AG combined with FPG or HbA1c improved the efficiency of diabetes screening. Saliva 1,5-AG is robust in nonfasting measurements and a noninvasive and convenient tool for diabetes screening.

Disclosure

Y. Bao: None. C. Jian: None. A. Zhao: None. X. Ma: None. K. Ge: None. W. Lu: None. W. Zhu: None. Y. Wang: None. J. Zhou: None. W. Jia: None.
895-P: Improved Glycemic Control with Insulin Glargine 300 U/mL vs. Glargine 100 U/mL in Type 1 Diabetes
Authors: PAOLA LUCIDI , CIOLI PATRIZIA , CANDELORO PAOLA , ANDREOLI ANNA MARINELLI , BOLLI GEREMIA B. , FANELLI CARMINE G. , PORCELLATI FRANCESCA
Published: June 2020

PK/PD of clinical doses of Glargine 300 U/mL (Gla-300) and Gla-100 differ (Diabetes Care 2019;42:85), but any clinical relevance has been difficult to demonstrate.

Aim: to demonstrate that PK/PD differences of individual doses of Gla-300 vs. Gla-100 translate into clinical benefits in T1DM, as shown by interstitial glucose (IG) (CGM).

Methods: 18 persons with T1DM (age 40±12 yrs, diabetes duration 26±12 yrs, A1C 7.2±0.5 %, 55±6 mmol/mol) were randomized to Gla-300 or Gla-100 titrated for 3 months, followed by 2 month washout, and alternate treatment for 3 more months. Titrated mealtime rapid-acting insulin analogs were continued with both Gla-300 and Gla-100. Blinded CGM (iPro, Medtronic) was used during last 2 weeks of each 3-month treatment period.

Results: After 3 months, A1C was no different with Gla-300 vs. Gla-100 (both 6.9±0.5 %, 52±6 mmol/mol). CGM revealed lower 24 h IG (Figure); % time in range (70-180 mg/dl) was higher (82±8 vs. 72±10), whereas % time above range (>180 mg/dl, 16±7 vs. 24±8), % time below range (particularly from midnight to pre-breakfast) (<70 mg/dl, 2±3 vs. 4±4) and IG nocturnal variability (% CV 25±7 vs. 37±12) were lower with Gla-300 vs. Gla-100 (p<0.05). Dose was higher with Gla-300 (0.35±0.08 U/kg) vs. Gla-100 (0.28±0.07 U/kg).

Conclusions: Titrated Gla-300 improves glucose control, reduces variability and hypoglycemia risk vs. Gla-100, as predicted from PK/PD differences of individual doses.

Disclosure

L. Paola: None. P. Cioli: None. P. Candeloro: None. A. Marinelli Andreoli: None. G.B. Bolli: Consultant; Self; Sanofi. C.G. Fanelli: Advisory Panel; Self; Eli Lilly and Company, Sanofi. F. Porcellati: None.

Funding

Sanofi
910-P: Relationship between Continuous Glucose Monitoring Metrics and Oxidative Stress in Patients with Type 2 Diabetes Mellitus: A Cross-Sectional Study
Authors: KOHATA YO , OHARA MAKOTO , FUJIKAWA TOMOKI , NAGAIKE HIROE , KUSHIMA HIDEKI , HIROMURA MUNENORI , TERASAKI MICHISHIGE , MORI YUSAKU , FUKUI TOMOYASU , HIRANO TSUTOMU , YAMAGISHI SHO-ICHI
Published: June 2020

Aims: Standardized continuous glucose monitoring (CGM) metrics for clinical care were announced in 2019. There have been no reports, however, on the relationship between standardized CGM metrics and oxidative stress. We therefore decided to investigate the relationships between standardized CGM metrics, classical glycemic variability, and oxidative stress.

Methods: This study was a cross-sectional analysis of 117 patients with type 2 diabetes mellitus (T2DM). Oxidative stress was estimated using the diacron-reactive oxygen metabolites (d-ROMs) test. The following parameters were used as CGM metrics: mean glucose level (MGL), percentage coefficient of variation for glucose (%CV), time above range (TAR), time in range (TIR), time below range (TBR), standard deviation (SD), and mean amplitude of glycemic excursions (MAGE), a classic index.

Results: A total of 117 patients (mean age of 64.1 ± 12.6 years, mean disease duration of 13.1 ± 11.5 years, and HbA1c of 8.3 ± 1.5%) who met the study inclusion criteria were finally analyzed. The univariate analysis showed that age, triglyceride, HbA1c, MGL, %CV, SD, MAGE, and TAR were significantly correlated with d-ROMs. Further, a stepwise multiple regression analysis identified SD, MAGE, and sex as independent contributors to d-ROMs.

Conclusions: Oxidative stress was associated with the SD and MAGE, two parameters affected by the mean glucose level, as CGM metrics in patients with T2DM.

Disclosure

Y. Kohata: None. M. Ohara: None. T. Fujikawa: None. H. Nagaike: None. H. Kushima: None. M. Hiromura: None. Y. Mori: Research Support; Self; Taisho Pharmaceutical Co., Ltd. T. Fukui: None. T. Hirano: None. S. Yamagishi: None.
914-P: Predictors of ED and Inpatient Admissions after Hypo- and Hyperglycemic Events: Leveraging Remote Monitoring Data of People with Diabetes
Authors: LU WEI , JAMES ROBERTA , PAINTER STEFANIE L. , SHAH BIMAL
Published: June 2020

Background: Predictive models to identify people with diabetes mellitus (DM) at high-risk for future ED visits and inpatient admissions (IA) are an area of clinical interest. The models do not include self-monitoring blood glucose (SMBG) levels. Livongo, the leader in Applied Health Signals, includes cellular-enabled BG meter that allows instantaneous uploading of SMBG values into the cloud with millions of values in people with DM.

Methods: Leveraging data collected via the Livongo program with medical and pharmacy claims, machine learning techniques were used to identify the 25 most predictive variables of severe hypo- and hyperglycemia (BG ≤54 mg/dL and ≥400 mg/dL, respectively) resulting in an ED or IA encounter within three days. Four models were constructed for DM (type 1 and type 2) and encounter (ED and IA). Participants had to be enrolled for at least 12 months with continuous medical benefits eligibility for 24 months. Variable categories included in modeling were demographics, comorbidities based on ICD-10 codes, prior HCU, new and current medications, 30-days SMBG patterns with mean BG levels, and months on program. Area under the curve (AUC) was used to assess model performance.

Results: There were 7,633 people selected. They had a mean age of 54 years, 48% were female, and 11% had type 1 DM. In this group, 924 and 1,518 severe hypo- and hyperglycemic with ED or IA encounters occurred. Random forest models had the highest AUC with values greater than 98% and sensitivity and specificity above 93% and 99%, respectively. HCU variables were the most predictive variables in all 4 models. Mean 7-day BG level, 30-day count of BG checks, and before-breakfast checking were also highly predictive.

Conclusions: SMBG variables are independent predictors of hypo- and hyperglycemia with ED and IA encounters. Real-time BG remote monitoring programs have the capability to identify people at high-risk of costly HCU and develop interventions to improve care.

Disclosure

W. Lu: Employee; Self; Livongo Health. R. James: Advisory Panel; Self; AstraZeneca. Employee; Self; Livongo Health. S.L. Painter: Employee; Self; Livongo Health. B. Shah: Employee; Self; Livongo Health.
917-P: Features of Glucose Profiles in Newly Diagnosed Fulminant Type 1 Diabetes Mellitus
Authors: GAN LU , LIN BEISI , ZHOU YONGWEN , YAN JINHUA , XU WEN , YANG DAIZHI , LUO SIHUI , ZHENG XUEYING , LIANG HUA , YAO BIN , WENG JIANPING
Published: June 2020

Fulminant type l diabetes mellitus (FT1DM) is a subtype of T1DM with acute onset, but data on its feature of glucose profiles is limited. This study aimed to compare the glucose profiles between newly diagnosed patients with FT1DM and typical type 1 diabetes mellitus (TT1DM). From Sep. 2013 to Oct. 2019, newly diagnosed patients who fulfilled respective criteria of FT1DM and TT1DM in our department were included in the analysis. After diabetic ketoacidosis correction, the MiniMed Paradigm 722 insulin pump was used to monitor the real-time glucose levels for 3 consecutive days. Demographic and clinical data at the onset were collected. CGM metrics were compared between the two groups. A total of 8 patients (female, 50%) were diagnosed as FT1DM with 2 of them developed diabetes during pregnancy, and 20 patients (female, 60%) were diagnosed as TT1DM. At the onset, patients in FT1DM group had higher age (30.5 [24.0, 38.0] vs. 21.0 [9.5, 32.3] yrs, P<0.01), lower level of HbA1c (6.31 ± 0.58 vs. 11.73 ± 3.47%, P<0.001) and C-peptide (0h, 0.04 [0.04, 0.04] vs. 0.06 [0.04, 0.11] nmol/L; 2h, 0.04 [0.04, 0.04] vs. 0.19 [0.14, 0.27] nmol/L, all P<0.05) than those with TT1DM. Larger glycemic fluctuation was observed in FT1DM group with significantly higher level of SD (3.2 ± 0.8 vs. 2.5 ± 0.8 mmol/L, P<0.05), CV (36.14 ± 7.40 vs. 29.55 ± 5.69%, P=0.02) and MAGE(8.10 ± 1.46 vs. 6.25 ± 1.92 mmol/L, P=0.02). The five key metrics were similar between the two groups: mean glucose (8.5 [7.7, 9.1] vs. 7.8 [7.1, 9.0]mmol/L), glucose management indicator (6.9 [6.5, 7.4] vs. 6.5 [6.1, 7.3]%), time spent in range 3.9-10.0mmol/L (72.6 [60.8, 79.5] vs. 78.6 [69.1,89.8]%), time spent <3.9 mmol/L (1.2 [0.1, 4.1] vs. 0.1 [0.0, 2.5]%) and time spent >10.0mmol/L (27.2 [16.4, 35.1] vs. 18.7 [8.7, 28.9]%) (all P>0.05).Newly diagnosed patients with FT1DM had significantly larger glycemic fluctuation than those with TT1DM even with a well-controlled inpatient management.

Disclosure

L. Gan: None. B. Lin: None. Y. Zhou: None. J. Yan: None. W. Xu: None. D. Yang: None. S. Luo: None. X. Zheng: None. H. Liang: None. B. Yao: None. J. Weng: None.

Funding

National Key Research and Development Program of China (2017YFC1309600); Natural Science Foundation of Guangdong Province (2018A030313251)
923-P: Improved Glycemic Variability and Control without Increased Risk of Hypoglycemia when Linagliptin Is Added to Glimepiride Therapy in Patients with HNF1A-Diabetes
Authors: CHRISTENSEN ALEXANDER S. , HAEDERSDAL SOFIE , STØY JULIE , STORGAARD HEIDI , KAMPMANN ULLA , SEGHIERI MARTA , HOLST JENS J. , HANSEN TORBEN , KNOP FILIP K. , VILSBØLL TINA
Published: June 2020

Sulfonylurea (SU) is first-line treatment of hepatocyte nuclear factor 1-alpha (HNF1A)-diabetes but has limitations due to the risk of hypoglycemia. We hypothesized that SU in combination with the dipeptidyl peptidase-4 inhibitor linagliptin would provide an efficacious treatment with less glycemic variability and hypoglycemia in patients with HNF1A-diabetes compared with SU monotherapy. In a randomized, double-blinded, crossover trial, patients with HNF1A-diabetes (n = 19, [mean ± SD]; age: 43 ± 14 years; BMI 24 ± 3 kg/m2; HbA1c 7.4 ± 0.2% [57 ± 7 mmol/mol]) were randomly assigned to treatment with glimepiride + linagliptin (16 weeks), wash-out (4 weeks) and treatment with glimepiride + placebo (16 weeks) (or vice versa). Treatments were evaluated by continuous glucose monitoring (CGM), HbA1c and a meal test. Compared with glimepiride + placebo, glimepiride + linagliptin showed significant improvements in glycemic variability (assessed as coefficient of variation (CV) and SD on CGM), HbA1c and body weight (Figure 1). Incidence of hypoglycemia (assessed by patient-reported episodes and CGM) was similar between treatments.

In conclusion, linagliptin as add-on treatment to SU in patients with HNF1A-diabetes improved glycemic variability, glycemic control and body weight without increasing the risk of hypoglycemia.

Disclosure

A.S. Christensen: None. S. Haedersdal: None. J. Støy: None. H. Storgaard: None. U. Kampmann: Stock/Shareholder; Spouse/Partner; Novo Nordisk A/S. M. Seghieri: None. J.J. Holst: Advisory Panel; Self; AstraZeneca, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Zealand Pharma A/S. Other Relationship; Spouse/Partner; Antag Therapeutics. T. Hansen: None. F.K. Knop: Advisory Panel; Self; AstraZeneca, Merck Sharp & Dohme Corp., Mundipharma International, Novo Nordisk A/S, Sanofi. Consultant; Self; Carmot Therapeutics, Inc., Eli Lilly and Company, Novo Nordisk A/S. Research Support; Self; AstraZeneca, Gubra, Novo Nordisk A/S, Sanofi, Zealand Pharma A/S. Speaker’s Bureau; Self; AstraZeneca, Lupin Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Norgine B.V., Novo Nordisk A/S. T. Vilsbøll: Advisory Panel; Self; AstraZeneca, Mundipharma International, Novo Nordisk A/S, Sun Pharmaceutical Industries Ltd. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Lilly Diabetes, Medscape, Merck Sharp & Dohme Corp., Sanofi.
929-P: Dulaglutide Shows Sustained A1C Reduction: Two-Year U.S. Real-World Study
Authors: MODY REEMA , YU MARIA , GRABNER MICHAEL , BOYE KRISTINA , TENG CHIA-CHEN , KWAN ANITA
Published: June 2020

The primary objective of this retrospective, observational study was to evaluate glycemic control over a 2 yr follow-up period among dulaglutide (DU) initiators with continuous treatment using U.S. claims data from the HealthCore Integrated Research Database (HIRD®) between May 2014 and May 2019 (index date=earliest DU fill during index period between November 2014 and May 2017). Patients (pts) ≥18 yrs old with T2D, ≥1 pharmacy claim for DU during the index period, continuous enrollment in the 6 months pre index (=baseline [BL]) and 2 yrs post index, ≥1 claim for DU or ≥60 days supply in every quarter during the 2-yr follow-up period, and ≥1 A1C result at both BL and 2 yrs were included. At BL, pts (N=872; 47.5% female) were (mean ± SD) 54.5 ± 8.2 yrs old with an A1C of 8.7 ± 1.8%. Antihyperglycemic medication use pre/post index included: insulin, 28%/35%; DPP-4 inhibitors, 37%/20%; SGLT2 inhibitors, 29%/44%. More than two-thirds were being treated for dyslipidemia, hypertension, or CVD. Key results are included in the Figure. Significant A1C reduction was observed from BL to 2 yrs (-1.3%, p<0.0001). Forty-three percent of pts achieved an A1C <7% and 74% achieved an A1C <8% at 2 yrs. In this real-world study among DU initiators with continuous treatment, a clinically significant reduction in A1C was seen at the 3-month assessment and persisted for up to 2 yrs. These data support the use of DU as an effective long-term treatment for T2D in clinical practice.

Disclosure

R. Mody: Employee; Self; Eli Lilly and Company. M. Yu: Employee; Self; Eli Lilly and Company. Employee; Spouse/Partner; LifeLabs. Stock/Shareholder; Self; Eli Lilly and Company. M. Grabner: Research Support; Self; Eli Lilly and Company. K. Boye: Employee; Self; Lilly Diabetes. Stock/Shareholder; Self; Eli Lilly and Company. C. Teng: Research Support; Self; Eli Lilly and Company. A. Kwan: Employee; Self; Eli Lilly and Company. Employee; Spouse/Partner; Eli Lilly and Company. Stock/Shareholder; Spouse/Partner; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company.