bluuebunny/crossref_metadata_2025_split huggingface dataset:
768-P: Efficacy and Safety of Low-Carb Diet of 50g/Day and 120g/Day in Obese Patients
Authors: KIKUCHI TAKAKO 
Published: June 2019

Objective: Nutrition therapy is indispensable in the management of obesity. Low-carb diet is one of the recommended diets. On the other hand, there is little evidence on Low-carb diet in Japan. Besides, we don’t know the optimum amount of carbohydrate. So, we tried to ellucidate the efficacy and safeness of Low-carb diet, and to compare two amounts of Low-carb diet: 50g (LC50g) or 120g carb/day (LC120g).
Research Design and Methods: 42 Overweight or obese Japanese adults with one or more metabolic disorders (such as diabetes, hyperlipidemia, hypertension) were randomly assigned to a 3-month Low-Carb diet intervension (50g or 120g carb/day). All food was provided, and patients were assigned to report what they ate every day to the registered dietitian via mail, so that we could check compliance. Total calorie was calculated based on the ideal body weight and physical activity index. Every month, body weight, body composition (measured by multi-frequency BIA) were checked. Blood pressure, HbA1c, FPG, serum ketone body, kidney and liver function and lipid profile, serum C-peptide, urinary albumin, PWV/ABI were checked. We used T test for statistic analysis.
Results: No one dropped out. Median age and BMI were 43 and 28.5. Change in BW was -8.5kg in LC50g, -7.2kg in LC120g, there was no significant difference. AST, ALT, γGTP, LDH, Cre decreased after intervention in both groups. LDH, Cre significantly decreased in LC50g. Acetoacetic acid and EPA significantly increased in LC50g. Baseline analysis showed, BW at age 20, early rising, dinner time and exercise habits were related to susceptibility to losing weight. A questionnaires survey revealed patients felt more stressful in LC50g.
Conclusion: In this study, both amount of carb restriction was effective and safe, and there was no significant difference in losing weight, diabetes, lipid and hypertension control. LC120g was easy to carry out. Finally, this study is the one and only because it achieved high compliance by food offering and on time follow-up.

Disclosure
T. Kikuchi: None.
77-LB: Digital Behavioral Counseling in a Workforce Setting Reduces 8-Year Risk of Developing Type 2 Diabetes
Authors: BIRSE CHARLES E. , FRAGALA MAREN S. , SATISH ANITA , MCNAMARA KELSEY C. , CASTRO SWEET CYNTHIA M. , LAGIER ROBERT J. 
Published: June 2019

Background: Digital behavioral counseling has previously been shown to reduce levels of risk factors for diabetes including fasting glucose and HbA1c. We asked whether the 8-year risk of developing diabetes could be reduced within the first year of a digital counseling program.
Methods: Individuals were included in the study if they (i) participated in an employer-sponsored wellness program with year-end biometric screening for 3 consecutive years (2016, 2017, and 2018), (ii) had prediabetes-range fasting glucose (100 to 125 mg/dL) or Hb1Ac (5.7% to 6.4%), (iii) a BMI ≥25 kg/m2 at the end of 2017, and (iv) completed at least 9 lessons in a digital behavioral counseling program initiated at the beginning of 2018. Paired t-Tests were used to assess changes in yearly trajectories of diabetes risk factors before and after intervention (Table).
Results: The baseline characteristics of the cohort at the 2017 year-end screen are provided (Table). On average, participants (n=460) were engaged in the counseling program for 9.1 months (SD: 1.2) and completed 25.1 lessons (SD: 12.3). After participation, levels of all biometric variables included in the 8-year risk model* shifted in a favorable direction; the 8-year risk of developing diabetes was reduced (Table).
Conclusions: Digital behavioral counseling was effective in reducing risk factors and the 8-year risk of diabetes during the first year of the program.

Disclosure
C.E. Birse: Employee; Self; Quest Diagnostics. Stock/Shareholder; Self; Quest Diagnostics. M.S. Fragala: Employee; Self; Quest Diagnostics. A. Satish: None. K.C. McNamara: Employee; Self; Omada Health, Inc. C.M. Castro Sweet: Employee; Self; Omada Health, Inc. R.J. Lagier: Employee; Self; Quest Diagnostics.
784-P: Effects of Low-Carb and Low-Fat Dietary Strategies on Lipid Profile in Subjects with Prediabetes—DiNA-P
Authors: MEYER NINA , DIEKMANN LAVINIA MURIELLE , KABISCH STEFAN , DAMBECK ULRIKE , PFEIFFER ANDREAS F. 
Published: June 2019

Prediabetes as precursor of diabetes is characterized by a disorder in glucose metabolism, but is often associated with an impaired fat metabolism, too, defined by higher serum levels of cholesterol and/or triglycerides. Hyperlipidemia and a poor LDL/HDL ratio represent important risk factors for cardiovascular diseases. The risk is even higher in presence of NAFLD, a common comorbidity in subjects with (pre-)diabetes. Thus, amelioration of these metabolic disorders is important. Both hyperglycemia and hyperlipidemia can initially be treated by dietary approaches. Low-fat diets, but possibly even more low-carb diets seem to improve metabolic parameters.
Thus, we compare the effect of a low-carb or low-fat diet in subjects with prediabetes, focusing on predefined lipid parameters and liver fat, investigating possible mechanisms by which cardiometabolic risk factors can be reduced.
Our analysis is conducted within a preliminary sample of the ongoing DiNA-P (Diabetes Nutrition Algorithms in Prediabetes) study, covering 12 months of dietary intervention in 176 subjects of which 155 completed the study by now. Lipid metabolism is assessed by total cholesterol, LDL, HDL, LDL/HDL ratio and triglyceride serum levels, liver fat by magnetic resonance spectroscopy. Statistical analysis is based on within-group and between-group comparisons as well as non-parametric correlation analysis (Spearman). Level of significance is p&lt;0,05.
Within the low-fat group, an improvement in all lipid parameters except for HDL cholesterol can be stated. Also, the LDL/HDL ratio as well as liver fat content were reduced in the end of the study. Within the low-carb group, the same favorable development was observed. Additionally, HDL-cholesterol showed a significant improvement, resulting in a significantly different development of HDL cholesterol in the low-carb group compared to low-fat. Our findings are confirmed by subgroup analysis, stratified by use of lipid lowering medication.

Disclosure
N. Meyer: None. L. Diekmann: None. S. Kabisch: Research Support; Self; California Walnut Commission, Institute for Grain Processing, Nuthetal, J. Rettenmaier Söhne, Südzucker / Beneo. Other Relationship; Self; Berlin-Chemie AG, Sanofi. U. Dambeck: None. A.F. Pfeiffer: Advisory Panel; Self; Abbott, Berlin-Chemie AG, Novo Nordisk A/S. Speaker's Bureau; Self; Lilly Diabetes, Novartis AG, Sanofi-Aventis Deutschland GmbH.
786-P: Serum Dehydroepiandrosterone Sulfate Concentration and Animal Protein Intakes Are Important Factors for Skeletal Muscle Mass in Japanese Patients with Type 2 Diabetes
Authors: MOYAMA SHOTA , KUWATA HITOSHI , HAMAMOTO YOSHIYUKI , YABE DAISUKE , SEINO YUTAKA 
Published: June 2019

The aim of this study was to examine the association between serum reproductive hormone concentrations, dietary intakes and skeletal muscle mass in patients with type 2 diabetes. This cross-sectional study investigated 54 Japanese patients with type 2 diabetes (32 males) during the period between 2014 to 2018. All patients completed measurements of serum dehydroepiandrosterone sulfate concentration (DHEA-S), skeletal muscle index (SMI) and dietary intakes at same period. Those who had adrenal tumor, thyroid cancer, and thyroid dysfunction were excluded. In the analysis, as a first-step, we conducted single regression analysis between age, DHEA-S, SMI, total energy and each macronutrient intakes. SMI was positively correlated with total energy and protein intakes. Interestingly, while protein intakes derived from animal foods were positively correlated with SMI, those from plant foods showed negative correlation. On the other hand, DHEA-S was positively correlated with SMI. Then, multiple regression analysis (MRS) was performed to examine the contributors to SMI (Table). MRS revealed the association of SMI with DHEA-S and protein intakes. Our data suggest that DHEA-S and protein intakes are the factors contribute to SMI in Japanese patients with type 2 diabetes.

Disclosure
S. Moyama: None. H. Kuwata: Research Support; Self; Eli Lilly and Company. Y. Hamamoto: Speaker's Bureau; Self; Novo Nordisk A/S. D. Yabe: Advisory Panel; Self; Abbott. Research Support; Self; Astellas Pharma Inc. Speaker's Bureau; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Sanofi K.K., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited. Y. Seino: Research Support; Self; Arkray, Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited, Terumo Medical Corporation.
788-P: Effects of Sucralose Ingestion vs. Sucralose Taste on Metabolic Responses to a Glucose Load in Subjects with Normal Weight and Obesity
Authors: M. YANINA PEPINO , NICHOL ALEXANDER D. , ROTHER KRISTINA , SALAME CLARA 
Published: June 2019

Objective: Consumption of low-calorie sweeteners (LCS) is associated with abnormalities of glucose metabolism. Here we evaluate the effects of sucralose ingestion, versus sucralose taste (i.e., sham feeding), on metabolic responses to a labeled oral glucose tolerance test (OGTT) in subjects with normal weight and obesity.
Research Design and Methods: Ten normal-weight (BMI: 23±1 kg/m2) and 11 subjects with obesity (BMI: 38±6 kg/m2), all non-habitual users of LCS, underwent 3 dual-tracer OGTTs preceded by consuming 48 mg of sucralose, water, or sham feeding sucralose in randomized order. Indices of β-cell function and insulin sensitivity (SI) were estimated by using oral minimal models of glucose, insulin and C-peptide kinetics.
Results: Compared to water, sucralose ingestion (but not sham feeding) increased the glucose incremental area under the curve by 30±10% in both groups. In contrast, sucralose had different effects on plasma insulin concentrations in these groups (P&lt;0.01). Sucralose ingestion decreased the magnitude of the glucose-induced increase in insulin concentrations 20-40 min post-glucose load in normal-weight subjects, but increased it 90-120 min post-glucose load in subjects with obesity. Sucralose sham-fed similarly decreased the magnitude of the glucose-induced increase in insulin concentrations within 60 min of the OGTT in both groups. In addition, sucralose ingestion (but not sham feeding) increased SI in normal-weight subjects by 52±20% but did not affect SI in subjects with obesity (P&lt;0.02). Sucralose did not affect glucose kinetics or indices of the sensitivity of the β-cell response to glucose in either group.
Conclusions: Acute sucralose ingestion differentially affects glucose metabolism in subjects with normal weight versus obesity. In addition, our findings underscore a physiological role for sweetness in glucose homeostasis, which supports the notion that sweetness should be used in moderation.

Disclosure
M. Pepino: None. A.D. Nichol: None. K. Rother: None. C. Salame: None.

Funding
National Institutes of Health (P30DK020579, P60DK020579, P30DK056341); National Institute of Diabetes and Digestive and Kidney Diseases
789-P: Association of Zinc Intake with Obesity in Japanese Patients with Type 2 Diabetes Mellitus (T2DM)
Authors: NEDACHI RINA , FUJIHARA KAZUYA , HATTA MARIKO , MATSUBAYASHI YASUHIRO , TAKEDA YASUNAGA , ISHII DAI , HORIKAWA CHIKA , KATO NORIKO , MAEGAWA HIROSHI , SONE HIROHITO ,   JDDM GROUP
Published: June 2019

Obesity is associated with deterioration of T2DM and its complications. Although low serum zinc levels are reportedly associated with obesity in nondiabetic people, clinical evidence regarding the association between dietary intake of zinc and obesity in diabetic individuals is sparse. Thus, we aimed to clarify this issue utilizing our nationwide registry of Japanese patients with diabetes. Analyzed were 1891 patients with T2DM (mean age, 62 y; men, 62%) whose data from a food frequency questionnaire were available. Obesity was defined as BMI of ≥25. The associations between obesity and intake of zinc were determined by multivariate regression as well as quintile analyses. Odds ratio (OR) for obesity was 0.74 (0.65, 0.84) per 1 mg increase in zinc intake. Quintile analysis revealed a stepwise decrease in ORs for obesity at higher quintiles. ORs in Q3-Q5 were significant compared to Q1 as the reference (Table). Food groups strongly related to zinc intake among our patients were meats (correlation coefficients, 0.64), fish (0.49), and vegetables (0.47). These results suggest close associations between zinc intake and obesity in patients with T2DM. This information would be clinically applicable for nutrition education.

Disclosure
R. Nedachi: None. K. Fujihara: None. M. Hatta: None. Y. Matsubayashi: None. Y. Takeda: None. D. Ishii: None. C. Horikawa: None. N. Kato: None. H. Maegawa: Research Support; Self; Antares Pharma, Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Mitsubishi Tanabe Pharma Corporation, Takeda Pharmaceutical Company Limited. Speaker's Bureau; Self; Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Takeda Pharmaceutical Company Limited. H. Sone: Research Support; Self; Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Kowa Pharmaceutical Europe Co. Ltd., Kyowa Hakko Kirin Co., Ltd., Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited.
79-LB: The Relationship between Diabetes Distress and Technology Experience in a Closed-Loop Control (CLC) Trial
Authors: GONDER-FREDERICK LINDA , BISIO ALESSANDRO , BROWN SUE A. , KOLLAR LAURA L. , EMORY EMMA , KOVATCHEV BORIS 
Published: June 2019

At present, little is known about the relationship between emotional well-being and use of diabetes technology, such as closed loop control. This study examined the association between diabetes-related distress and technology opinions/experience in a cohort of people with type 1 diabetes participating in a CLC clinical trial. The trial involved use of sensor-augmented pump (SAP) therapy, overnight only CLC (ON CLC), and 24-hr CLC (24/7 CLC) over 8-week treatment periods. Participants (n=61, 60% F, age= 42.5 ± 11.6, T1D ys = 22.1 ± 12, BMI=29.6 ± 5.5, HbA1c at baseline = 7.4 ± 1.0 TDI/kg = 0.6 ± 0.2) completed the Diabetes Distress Scale (DDS) at baseline and the Closed Loop User Opinion Survey (CLUOS) following each treatment condition. The CLUOS was validated using factor analysis which yielded two subscales describing positive and negative experiences with the technologies (i.e., benefits and barriers), with all items loading .32 or higher on their respective subscales, and Cronbach’s α coefficients of .95 and .93, respectively. Participants were divided into two groups based on baseline DDS scores using the median score to create high and low distress groups for each DDS subscale. T-tests were used to compare CLUOS subscale scores in the high and low DDS groups. There were no differences on the Regimen, Physician or Interpersonal DDS subscales. However, individuals scoring higher on the Emotional DDS subscale at baseline had higher scores on the Negative CLUOS subscale after the SAP (p=0.024) and the ON CLC (p=0.039) conditions, and a trend toward higher Negative scores after the 24/7 CLC (p=0.061). Although preliminary, these findings suggest that higher levels of emotional diabetes distress should be considered and perhaps addressed prior to implementing new diabetes technologies and that higher emotional distress levels may increase the probability of negative experiences with technology, possibly limiting its uptake and use.

Disclosure
L. Gonder-Frederick: Employee; Self; HFS-Global. Research Support; Self; Dexcom, Inc., Jaeb Center for Health Research, Tandem Diabetes Care. Speaker’s Bureau; Self; Diabetes UK. Stock/Shareholder; Self; BeHealth Solutions. A. Bisio: None. S.A. Brown: Research Support; Self; Ascensia Diabetes Care, Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care. L.L. Kollar: None. E. Emory: None. B. Kovatchev: Advisory Panel; Self; Sanofi. Board Member; Self; TypeZero Technologies, Inc. Consultant; Self; Sanofi, Tandem Diabetes Care. Research Support; Self; Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care. Speaker’s Bureau; Self; Dexcom, Inc. Stock/Shareholder; Self; TypeZero Technologies, Inc. Other Relationship; Self; Johnson & Johnson, Sanofi.

Funding
National Institutes of Health (R01DK085623); Roche Diagnostics; Tandem Diabetes Care, Inc.; Dexcom, Inc.
79-OR: Fully Closed-Loop Using Faster vs. Standard Aspart in Type 2 Diabetes (T2D): A Double-Blind Randomised Crossover Trial
Authors: BALLY LIA , HERZIG DAVID , WILINSKA MALGORZATA E. , RUAN YUE , VOGT ANDREAS , WERTLI MARIA M. , VOGT BRUNO , STETTLER CHRISTOPH , HOVORKA ROMAN 
Published: June 2019

Aim: To evaluate the safety and efficacy of faster insulin aspart (FA) vs. insulin aspart (A) in T2D using fully closed-loop (CL) glucose control.
Methods: Fifteen adults (5 females, age 59±10 years, BMI 34.5±9.1kg/m2) with insulin-treated T2D (HbA1c 7.7±1.2% [60±13mM/mol], insulin dose 1.0±0.7U/kg) underwent two 22h-visits (1900-1700) receiving CL with either FA or A in a double-blind randomized crossover design. Basal-bolus regimen was replaced by model predictive control algorithm-directed s/c administration of FA or A based on sensor glucose levels. After an overnight stay, participants consumed matched breakfast and lunch and performed a 20 min-walk. Plasma glucose measurements every 15 min over 10h (0700-1700) evaluated CL performance.
Results: The proportion of time when plasma glucose was in target range from 5.6-10.0mM did not differ between FA and A (primary endpoint, p=0.17, Table). Mean glucose and glucose variability were comparable, as was time spent below and above target. The 2h-postprandial glucose increment was comparable after breakfast and significantly lower after lunch with FA vs. A (p=0.78 and p=0.047). One hypoglycaemic event (&lt;3.5mM) occurred with FA and two with A. Insulin dose was significantly higher with FA vs. A (p=0.021).
Conclusion: Short-term use of fully CL with FA vs. A requires higher insulin doses to achieve comparable glucose control.

Disclosure
L. Bally: None. D. Herzig: None. M.E. Wilinska: None. Y. Ruan: None. A. Vogt: None. M.M. Wertli: None. B. Vogt: None. C. Stettler: None. R. Hovorka: Advisory Panel; Self; Novo Nordisk A/S. Research Support; Self; Abbott, Dexcom, Inc., Medtronic. Speaker's Bureau; Self; Eli Lilly and Company, Novo Nordisk A/S. Other Relationship; Self; B. Braun Medical Inc., Medtronic.

Funding
UDEM Scientific Fund; Swiss Diabetes Foundation; Diabetes Centre Bern
790-P: The Amount of Carbohydrate in the Diet Affects Glucose Dynamics Day and Night
Authors: SCHMIDT SIGNE , RANJAN AJENTHEN , CHRISTENSEN MERETE B. , NØRGAARD KIRSTEN 
Published: June 2019

Aim: To investigate the effects of a low carbohydrate diet (&lt;100 grams/day) and a high carbohydrate diet (&gt;250 grams/day) on daytime (6:00-0:00) and nighttime (0:00-6:00) glucose dynamics in adults with type 1 diabetes.
Methods: Fourteen participants using sensor-augmented insulin pumps were included in a randomized crossover study with two 12-week intervention arms separated by a 12-week washout. Individual meal plans meeting the carbohydrate criteria were made for each study participant.
Results: Participant baseline characteristics were (mean ± SD): age 44 ± 12 years, BMI 24.8 ± 2.0 kg/m2, HbA1c 7.5 ± 0.4%, daily carbohydrate intake 152 ± 43 grams/day. Daily carbohydrate intake during the two intervention periods was (mean ± standard deviation) 98 ± 11 grams and 246 ± 34 grams, respectively. Distribution of glucose values are reported in the Table.
Discussion: Daytime hypoglycemia and nighttime hyperglycemia were lower during the low than the high carbohydrate diet. Further, more time was spent in normoglycemia at night during the low carbohydrate diet. These findings may be the results of smaller daytime meal boluses and fewer carbohydrates in the evening meal.

Disclosure
S. Schmidt: Speaker's Bureau; Self; Novo Nordisk A/S. A. Ranjan: None. M.B. Christensen: None. K. Nørgaard: Advisory Panel; Self; Abbott, Medtronic, Novo Nordisk A/S. Speaker's Bureau; Self; Bayer US, Medtronic, Roche Diabetes Care, Rubin Medical, Sanofi, Zealand Pharma A/S. Stock/Shareholder; Self; Novo Nordisk A/S.

Funding
Danish Diabetes Academy
80-OR: 670G Clinical Experience
Authors: LAL RAYHAN , BASINA MARINA , MAAHS DAVID M. , BUCKINGHAM BRUCE A. , HOOD KOREY K. , CONRAD BARRY P. , LEVERENZ JEANNINE , CHMIELEWSKI ANNETTE , PETERSON KRISTINE , WILSON DARRELL 
Published: June 2019

Objective: In September 2016, the FDA approved the Medtronic 670G “hybrid” closed-loop system. In “Auto Mode,” this system automatically controls basal insulin delivery based on CGM data, but requires users to enter carbohydrate intake and blood sugar for boluses. Studies show improved time in range associated with the use of Auto Mode. In an effort to track our real-world experience with this first commercial closed-loop device, we followed pediatric and adult patients placed on the 670G.
Methods: This was a 1-year prospective observational study, recruiting 5/2017-9/2018, of patients with type 1 diabetes, ≥7 years, starting the 670G system.
Results: 84 patients started on the 670G (range 9-61 years), 26 (31%) were &lt;18 years. Figure 1 shows a violin plot of percentage time in Auto Mode at each visit. Upon starting Auto Mode, 96% of participants continued for the first week. For those with available data by 3 months 22% stopped using Auto Mode, at 6 months 30%, at 9 months 43% and by 12 months 50% (70% &lt;18, 40% ≥18). Reasons for discontinuation included CGM issues, too many alarms and lack of control. At 6 months there was a significant correlation between improved A1c and time in Auto Mode (r = 0.4, p = 0.01).
Conclusions: While use of closed-loop technology correlates with improved glycemic control, a focus on usability and human factors is necessary to ensure patients stay on treatment. Alarms and sensor calibration are a major patient concern, which next generation technology should alleviate.

Disclosure
R. Lal: Consultant; Self; Abbott. M. Basina: None. D.M. Maahs: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; Abbott, Sanofi. Research Support; Self; Dexcom, Inc., Tandem Diabetes Care. B.A. Buckingham: Advisory Panel; Self; ConvaTec Inc., Novo Nordisk Inc., Profusa, Inc. Consultant; Self; Medtronic MiniMed, Inc. Research Support; Self; Beta Bionics, ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Medtronic MiniMed, Inc., Tandem Diabetes Care. Other Relationship; Self; Insulet Corporation, Tandem Diabetes Care. K.K. Hood: Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc. Speaker's Bureau; Self; Johnson & Johnson Diabetes Institute. B.P. Conrad: None. J. Leverenz: None. A. Chmielewski: None. K. Peterson: None. D. Wilson: Advisory Panel; Self; Tolerion, Inc. Research Support; Self; Beta Bionics, Dexcom, Inc., Medtronic.

Funding
National Institute of Diabetes and Digestive and Kidney Diseases (T32DK007217); Seiler Fund