bluuebunny/crossref_metadata_2025_split huggingface dataset:
797-P: Impact of Carbohydrate Counting on Glycaemic Control in People with Type 1 and Type 2 Diabetes on Intensified Insulin Therapy
Authors: HERMANNS NORBERT , EHRMANN DOMINIC , KULZER BERNHARD 
Published: June 2020

This study involved 154 subjects with type 1 and type 2 diabetes (84.6% with type 1 diabetes, age 46.6 ±13.6 years; 52% female, A1c 8.3 ± 0.9 %) who were undergoing intensified insulin therapy. Participants completed a questionnaire (SMART) that measures the accuracy of estimating carbohydrates in 27 foods or in meals, using the multiple-choice method. At the same time, glucose data collected by a flash glucose monitoring system were analysed in the two weeks prior to completion of the SMART questionnaire. The level of estimation error regarding carbohydrates correlated significantly with glucose variability (CV; r=-0.29; p&lt;0.001), range of glucose values (r=0.33, p&lt;.001) and maximum glucose values (r=0.32, p&lt;.001). These significant effects remained even after adjustment for HbA1c. Interestingly, there was no significant correlation between estimation error and mean glucose values (r=0.012, p=0.882), time in range (r=-0.018; p=0.815) or percentage of hyperglycemic (&gt;180 mg/dl; r=0.015, p=0.843) or hypoglycemic phases (&lt;70 mg/dl; r=0.005; p=0.953). The negative correlation coefficient indicates that underestimation of the amount of carbohydrates in a meal is more significant in terms of glucose variability than overestimation of the amount of carbohydrates. The results suggest that a good estimation ability regarding carbohydrate content is associated with lower glucose variability but does not necessarily affect glucose levels.

Disclosure
N. Hermanns: Advisory Panel; Self; Roche Diabetes Care. Research Support; Self; Abbott, AstraZeneca, Berlin-Chemie AG. D. Ehrmann: Speaker’s Bureau; Self; Abbott, Berlin-Chemie AG, Glooko, Inc. Other Relationship; Self; Dexcom, Inc., Roche Diabetes Care. B. Kulzer: Research Support; Self; Abbott, AstraZeneca, Berlin-Chemie AG, Roche Diabetes Care. Speaker’s Bureau; Self; Abbott, Ascensia Diabetes Care, Berlin-Chemie AG, Eli Lilly and Company, Medtronic, Novo Nordisk Inc., Roche Diabetes Care.

Funding
Abbott Diabetes Care Germany
8-OR: Coronary Calcium Scoring to Select Asymptomatic Diabetic Patients with Coronary Artery Stenoses Suitable for Revascularization
Authors: BERKANE NARIMANE , COSSON EMMANUEL , PINTO SARA , CIUNGANU TIT-LIVIU , VALENSI PAUL 
Published: June 2020

The aim of the study was to test various criteria to select asymptomatic patients with diabetes to be screened for silent myocardial ischemia (SMI). This screening might improve cardiovascular prognosis through identification of coronary stenoses (CS) suitable for revascularization. Clinical/biological/imaging criteria including coronary artery calcium (CAC) score were considered. We selected 416 asymptomatic patients with diabetes and very high cardiovascular risk according to ESC-EASD-2019 criteria (either 3 risk factors (RFs) or diabetes duration&gt;20 years and one RF or peripheral vascular disease or target organ damage). These patients had had a stress myocardial scintigraphy to detect SMI, with a coronary angiography in case of SMI. A CAC score &gt;100UA was present in 182 patients (43.7%). SMI was present in 40 patients (9.6%); 28 of them had a coronary angiography. CS were found in 15 out of these 28 patients (54%) and 11/15 of them had a revascularization procedure (73.5%). We tested the performances of various criteria to identify patients with SMI and CS. Performing a scintigraphy (i) only in patients with peripheral artery disease and/or a severe nephropathy (n=141) would have led to miss 6 patients with CS suitable for revascularization; (ii) in all patients but those who had only 3 RFs (n=283) would have led to miss 2 of them and (iii) only in patients with peripheral vascular disease and/or a severe nephropathy and if not in those with a CAC score &gt;100 (n=239) would have led to miss none of them. The algorithm which requires CAC score measurement (iii) was the most cost-effective. Among the asymptomatic diabetic patients fulfilling the very high cardiovascular risk criteria according to ESC-EASD-2019 guidelines, screening for SMI and CS only those with peripheral artery disease or severe nephropathy or a high CAC score (&gt; 100AU) appears to be a good compromise, which allows not to miss patients with CS suitable for revascularization, at a controlled cost.

Disclosure
N. Berkane: None. E. Cosson: None. S. Pinto: None. T. Ciunganu: None. P. Valensi: None.
800-P: Sleep Quality for Adults with Type 1 Diabetes Using Multiple Daily Injections (MDI) of Insulin Flying across Multiple Time Zones: A Pilot Study
Authors: BEVIER WENDY C. , CASTORINO KRISTIN N. , FARFAN CHRISTIAN C. , HAROUSH GAL , MEFFORD MIKAYLA R. , AXELROD CEARA , NELSON KRISTEN , SPINK LISA A. , KERR DAVID 
Published: June 2020

Long-haul travel (flights &gt;6 hours) can be disruptive for people living with type 1 diabetes (T1D). As part of a study comparing insulin Degludec U100 (DEG) with insulin Glargine U100 (GLA) during long-haul travel (ClinicalTrials.gov Identifier: NCT03668808), we evaluated the impact of direction of travel on sleep quality and glucose control. The study began, after randomization to GLA or DEG, in Honolulu (HI) with a non-stop flight to New York (NY). After 72 hours, subjects returned non-stop to HI. Six weeks later subjects repeated travel using the other basal insulin. Sleep was monitored with ActiGraph wGT3x worn on the non-dominant wrist for 24 hours at the destination and blinded continuous glucose monitoring (CGM) using the Abbott Freestyle Libre Pro. Usual care was used for glucose monitoring. Jet lag was assessed by a validated questionnaire. Twenty-one subjects (13 women) mean (SD) age 34.2 (15.2) years and HbA1c 7.5 (1.2) % completed travel (Table). TST, SE, and median glucose were significantly different between NY and HI. Sleep also differed subjectively with “ease of going to sleep” and “sleep quality” better in NY vs. HI; “alertness on waking” and “jet lag” were not different. We found that traveling across multiple time zones from East to West was more disruptive to sleep and associated with higher average glucose levels for adults with T1D using MDI.

Disclosure
W.C. Bevier: None. K.N. Castorino: Research Support; Self; Abbott, Dexcom, Inc., Medtronic, Mylan, Novo Nordisk Inc. C.C. Farfan: None. G. Haroush: None. M.R. Mefford: None. C. Axelrod: None. K. Nelson: None. L.A. Spink: None. D. Kerr: Advisory Panel; Self; Novo Nordisk A/S, Sanofi-Aventis. Research Support; Self; Eli Lilly and Company. Stock/Shareholder; Self; Glooko, Inc.

Funding
Novo Nordisk
803-P: Odds of Depressive Symptoms Vary Based on Childhood A1C Trajectory
Authors: MAJIDI SHIDEH , GENO RASMUSSEN CRISTY R. , DRISCOLL KIMBERLY A. , ODONNELL HOLLY , YOUNGKIN ERIN M. , SNELL-BERGEON JANET K. 
Published: June 2020

Higher A1c in adolescents with T1D may result from consistently elevated A1c since diagnosis or increasing A1c across time. Identifying modifiable risk factors may allow for individualized care to reduce A1c. Prevalence of elevated (≥5) scores on the Patient Health Questionnaire-9 was examined in 888 youth ≥10 years. Five A1c trajectory groups (Figure) were identified using Proc Traj (i.e., mid increasing group had moderately elevated A1c with rising levels over time). The association between elevated depressive symptoms and A1c trajectory groups was examined. Prevalence of elevated depressive symptoms was 31%, and increased significantly across A1c trajectory groups: low stable =21%, mid stable =31%, high stable =41%, mid increasing =34%, and high increasing =52% (p&lt;0.0001). Odds of elevated depressive symptoms in the high increasing vs. low stable group was 3.6 (95% CI 1.9-6.8). Similarly, being in the high stable (OR 2.4, 95% CI 1.5-3.9), mid stable (OR 1.7, 1.2-2.5) and mid increasing (OR 1.9, 1.01-3.4) groups were associated with higher odds compared to being in the low stable group. Even mildly elevated depressive symptoms may contribute to persistently elevated or rising A1c throughout childhood. Screening, monitoring, and discussing depressive symptoms in youth may help prevent a rise in A1c over time. Future studies should assess whether A1c trajectory can be changed through early intervention of depressive symptoms.

Disclosure
S. Majidi: Advisory Panel; Self; Companion Medical. C.R. Geno Rasmussen: None. K.A. Driscoll: None. H. ODonnell: None. E.M. Youngkin: None. J.K. Snell-Bergeon: Stock/Shareholder; Self; GlaxoSmithKline plc.
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 &gt;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&lt;.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&lt;.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 &lt; 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 (&lt;7%), BMI (&lt;26.0 Kg/m2) and TC (&lt;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&gt;5 moderately increases the probability of a disease given a positive test and NLR&lt;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&gt;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&lt;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&lt;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&lt;0.05). Moreover, Totum-63 had a significant lowering effect on body weight (-0.07 ± 0.42 vs. +1.83 ± 0.57 Kg; p&lt;0.05) and waist circumference (-1.67 ± 0.73 vs. +2.81 ± 0.65 cm; p&lt;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&lt;0.05). HbA1c negatively correlated with Time in Range (70-99 mg/dl, r=-0.51, p&lt;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)