bluuebunny/crossref_metadata_2025_split huggingface dataset:
156-OR: Fear of Hypoglycemia in People with Type 1 Diabetes and Problematic Hypoglycaemia: A Mismatch between Worry and Behaviour?
Authors: MACLEAN RORY H. , JACOB PETER , HAYWOOD SEAN , CHOUDHARY PRATIK , HELLER SIMON R. , TOSCHI ELENA , KARIYAWASAM DULMINI , ANDERBRO THERESE C. , AMIEL STEPHANIE A. 
Published: June 2020

Problematic hypoglycemia complicates insulin therapy in &gt;25% of adults with type 1 diabetes (T1D). Previous work has identified relationships between cognitive barriers to hypoglycemia avoidance and impaired awareness of hypoglycemia (IAH) with recurrent severe episodes (SH). We investigated fear of hypoglycemia in people with IAH and SH enrolling in a trial of interventions to restore hypoglycemia awareness (“HARPdoc” ClinicalTrials.gov NCT02940873) and compared their data with T1D clinic attenders not enriched for problematic hypoglycemia.
Method: Adults with T1D (HARPdoc cohort; n=98, age 54±13 yr, 56% female, T1D duration 40±15 yr) and problematic hypoglycemia (Clarke score 5.5±1.0) completed the Hypoglycemia Fear Survey II (HFS), Hyperglycemia Avoidance Scale (HAS) and anonymous 12 month SH recall forms. The T1D clinic comparator group was n=467, age 46±14 yr, 50% female, T1D duration 30±13 yr.
Results: In preliminary analyses, the HARPdoc cohort (n=88) reported higher scores on total HFS (p=0.018) and its worry subscale (p&lt;0.0001) than the comparator. Behavior subscale scores were lower (p&lt;0.0001). In the HARPdoc cohort, 25% (n = 22) reported low worry about hypoglycemia despite high SH risk (vs. 8% in the comparator). People with high worry scored the HAS worry subscale higher (p&lt;0.001). The HARPdoc cohort were grouped by frequency of SH (&lt;2; 2 - 4; 5-12 and &gt;12 SH per yr). Worry scores increased with higher SH rates (p=0.004). Behavior scores increased (p=0.041) but not above the comparator 75th centile. In the ’maintaining high glucose’ subset of HFS behavior items, scores increased between the high SH rate groups (p=0.042) but not the lower rate groups.
Conclusion: The majority of people with significant hypoglycemia problems show appropriately increased worry but not corresponding increases in behavior scores. A subgroup show inappropriately low worry. Inability to change behaviors, and worry about hyperglycemia, may be barriers to avoiding SH.

Disclosure
R.H. Maclean: None. P. Jacob: None. S. Haywood: None. P. Choudhary: Advisory Panel; Self; Abbott, Eli Lilly and Company, Insulet Corporation, Medtronic. Research Support; Self; European Union, JDRF. Speaker’s Bureau; Self; Dexcom, Inc., Novartis AG, Novo Nordisk A/S, Sanofi-Aventis. S.R. Heller: Advisory Panel; Self; Eli Lilly and Company, Novo Nordisk A/S, Zealand Pharma A/S. Speaker’s Bureau; Self; AstraZeneca, Novo Nordisk A/S. Other Relationship; Self; MannKind Corporation. E. Toschi: None. D. Kariyawasam: None. T.C. Anderbro: None. S.A. Amiel: Advisory Panel; Self; Medtronic, Novo Nordisk A/S, Roche Pharma. Other Relationship; Self; Diabetes UK.

Funding
JDRF (4-SRA-2017-266-M-N)
1561-P: Racial Disparity May Exist in Patients with Diabetes Mellitus Hospitalized with Acute Pancreatitis
Authors: CHOI NICOLE , GARCIA MONIKA E. , ZAKHARY BISHOY L. , FIREK MATTHEW A. , KIM DANIEL I. , JACKSON CHRISTIAN S. , FIREK ANTHONY 
Published: June 2020

Introduction: Acute pancreatitis (AP) is the most common reason for hospitalizations for gastrointestinal disorders in the United States. Diabetes mellitus (DM) is known to increase the risk for AP by almost 3-fold. Single center studies have demonstrated demographic disparities in AP patients with DM. The identification of AP and DM patients at risk may provide clues to etiology and develop population preventative measures to lower AP-related hospitalizations. Our primary aim was to evaluate demographic trends in patients with DM and AP using a large national database.
Methods: We extracted data from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample database, from 2012-2014. International Classification of Diseases 9th Version, and used clinical modification codes for adult patients hospitalized with primary diagnosis of AP.
Results: There were 1,310,385 primary admissions for AP during the study period. 27.4% were diagnosed with AP-DM. Patients with AP-DM were older in age compared to AP only (p&lt;0.001). Majority of AP only patients were female (50.4%). Majority of patients with AP-DM were male (55.1% without complications and 50.7% with complications). Non-Hispanic Whites (NHW) had the highest prevalence of AP without DM. All other races had greater prevalence of AP-DM, including the largest discrepancy in African-Americans (AA) and Hispanics. For NHW, 67.6% did not have DM, 59.0% and 53.3% had DM without and with complications, respectively. For AA, 14.5% did not have DM, 18.8% and 24.4% had DM without and with complications, respectively.
Conclusions: This study is the first to demonstrate a racial disparity in AP patients with DM on a national scale. Patients with AP-DM were likely to be older, male, and AA compared to NHW, which is consistent with previous data. Multiple factors between NHW, AA and Hispanics which include but are not limited to genetic, socioeconomic, and cultural factors may be determinants of this disparity.

Disclosure
N. Choi: None. M.E. Garcia: None. B.L. Zakhary: None. M.A. Firek: None. D.I. Kim: None. C.S. Jackson: None. A. Firek: None.
1567-P: Burden of Diabetes Mellitus in Adults in the Americas, 1990-2017: Global Burden of Disease Study
Authors: COUSIN EWERTON , SCHMIDT MARIA I. , DUNCAN BRUCE B. 
Published: June 2020

Background: Many countries of the Americas have a high prevalence of diabetes, but the disease burden related to diabetes in the Americas has been little investigated. We aim to describe current diabetes burden in the Americas and changes from 1990 to 2017.
Methods: We used estimates from the 2017 GBD Study for incidence, prevalence, deaths, disability-adjusted life years (DALYs) and risk factors in American regions for those aged 25+.
Results: Age-standardized incidence increased 20.8% from 1990 to 2017. Crude prevalence increased 40%, reaching 11.7% (95%UI: 9.4% - 11.2%), the highest rates being in North America and Central Latin America. A total of 352,769 (95% UI 340,170-366,155) deaths due to diabetes occurred in 2017. Central Latin America and the Caribbean presented the highest disease burden. Age-standardized DALYs increased 18% overall, with marked variation across nations (Figure). Main risk factors were high BMI and dietary characteristics.
Conclusion: Burden of diabetes in the Americas is high, being disproportionately large in Central Latin America and the Caribbean. High BMI and inadequate nutrition appear to be the main culprits. Public policies aiming to decrease diabetes incidence by acting on these and other risk factors, and to provide effective and cost-efficient care for all people with diabetes are much needed.
Figure: Percentage change in the age-standardized rate of DALYs, 1990-2017.

Disclosure
E. Cousin: None. M.I. Schmidt: None. B.B. Duncan: None.

Funding
Bill & Melinda Gates Foundation; Research Support Foundation of Rio Grande do Sul
157-LB: Nocturnal Alarms: A Potential Barrier to Diabetes Technology Use
Authors: COBRY ERIN C. , BERGET CARI , MESSER LAUREL H. , WADWA R. PAUL , VIGERS TIMOTHY B. , PYLE LAURA , SLOVER ROBERT H. , DRISCOLL KIMBERLY A. , FORLENZA GREGORY P. 
Published: June 2020

High quality sleep is important for individuals with type 1 diabetes (T1D). Alarms associated with using diabetes technology may cause sleep disturbance. The objective of this study was to describe nighttime alarm frequency in a sample of youth using Hybrid Closed-Loop (HCL) technology and to compare alarm frequency between baseline Sensor Augmented Pump (SAP) and HCL.
Youth starting the 670G participated in an observational study. Data on sensor wear, HCL use, and frequency of nighttime alarms (10pm-6am) were collected while using 670G in SAP (before starting HCL), 2 weeks after starting HCL, and during clinic visits 3, 6, 9 and 12 months after starting HCL.
Forty-one youth (age 15 [12, 17] yrs, T1D duration 7.2 [4.6, 10.2] yrs, HbA1c 8.8±1.5%) were included. The median number of alarms per night with HCL use ranged from 1.4 to 2.0 across the study compared to a median of 2.1 alarms per night with baseline SAP use. There was no difference in alarm frequency with HCL use compared to SAP use (p=0.40, adjusted for percent HCL use).
Experiencing two device alarms per night is likely to cause significant sleep disruption for youth and their parents, suggesting device alarms may reduce quality of life and be a barrier to diabetes technology use. Further research is needed to assess the impact of device alarms on sleep disruption for youth with T1D and their parents, and to identify strategies to reduce nighttime alarm frequency for youth using diabetes technology.

Disclosure
E.C. Cobry: None. C. Berget: None. L.H. Messer: Consultant; Self; Capillary Biomedical, Inc., Clinical Sensors, Inc., Dexcom, Inc., Tandem Diabetes Care. R. Wadwa: Advisory Panel; Self; Eli Lilly and Company, Medtronic. Research Support; Self; Dexcom, Inc., Eli Lilly and Company, MannKind Corporation, Medtronic, Novo Nordisk Inc., Tandem Diabetes Care. T.B. Vigers: None. L. Pyle: None. R.H. Slover: None. K.A. Driscoll: None. G.P. Forlenza: Advisory Panel; Self; Medtronic. Consultant; Self; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care. Research Support; Self; Abbott, Dexcom, Inc., Insulet Corporation, Medtronic, Tandem Diabetes Care.

Funding
National Institutes of Health (K12DK094712); JDRF (5-ECR-2019-736-A-N)
1572-P: Neighborhood Investments and Cardiometabolic Health in Two Predominantly African-American Communities: A Natural Experiment Study
Authors: GARY-WEBB TIFFANY , DUBOWITZ TAMARA , BOGART TIMOTHY A. , DASTIDAR MADHUMITA GHOSH , TROXEL WENDY M. 
Published: June 2020

Background: Neighborhood disadvantage is a robust and independent risk factor for poor cardiometabolic health, however, causal relationships have been difficult to establish. We used a natural experiment design to examine neighborhood investment on cardiometabolic risk factors among a randomly selected cohort of residents from two, low-income, urban, and predominately African American matched neighborhoods.
Methods: The sample included 532 participants (80% female; mean age=59 years; mean income=$21,073). During the study period (2016-2018), the intervention neighborhood received substantially more publicly-funded investments (housing and commercial investment) than a demographically matched comparison neighborhood. Difference-in-difference analyses tested for differential change between the neighborhoods in BMI, HbA1c, HDL-c, and SBP and DBP. Covariates included age, sex, income, education, presence of children in the home, length of residence in neighborhood, and BMI (for all outcomes except BMI).
Results: Contrary to expectations, relative to the comparison neighborhood, we saw a net increase in DBP (β=3.00, SE=1.24; p =.02) and a marginally significant increase in SBP (β= 4.30, p = .07) in the intervention neighborhood. In contrast, for HbA1c and HDL-c, intervention neighborhood residents showed improvements in these outcomes, relative to the comparison neighborhood; however, the effects were non or marginally significant after covariate adjustment (p=.12 for HbA1c and p=0.06 for HDL-c). There were no significant changes in BMI.
Conclusion: Our findings suggest that the potential cardiometabolic benefits of neighborhood revitalization may manifest over a longer period of follow-up, and that there may be some interim consequences for certain health outcomes, in the process of neighborhood change. These findings highlight the importance of examining root causes of health disparities and using robust study designs.

Disclosure
T. Gary-Webb: None. T. Dubowitz: None. T.A. Bogart: None. M. Ghosh Dastidar: None. W.M. Troxel: None.

Funding
National Heart, Lung, and Blood Institute (HL131531)
1577-P: How Does Military Service Impact Risk for Diabetes in African Americans with a History of Adverse Childhood Experiences?
Authors: WARDIAN JANA L. , STONE EMILY 
Published: June 2020

Background: African Americans (AA) in the general population have double the odds for developing diabetes (DM). In addition, older age and a score of 4+ on the Adverse Childhood Experiences (ACE) inventory has been associated with increased risk for DM; education has been found to moderate risk. In addition, military service disproportionately attracts those with high ACE; however, educational opportunity is more readily available. We explored the effect of military service on the risk for developing diabetes in AA with high ACE.
Methods: We analyzed 2009-2012 BRFSS data from U.S. states opting to include an ACE module (n=117,084). AA (n=8715) comprise about 9.3% of this sample after weighted for representativeness.
Results: In the overall sample, age (OR= 1.04, 95% CI = 1.05-1.05), high ACE (4+; OR = 1.38, 95% CI =1.24-1.53), and being AA (OR = 2.1, 95% CI = 1.88-2.34) all significantly (p &lt; .001) increase likelihood of developing DM, and education reduces it (OR = 0.80, 95% CI = 0.77-0.83; p &lt; .001). Controlling for age, males are more likely to have a history of military service (OR = 23.7, 95% CI = 20.9-26.9, p &lt; .001). Those with high ACE are more likely to enlist (OR = 1.66, 95% CI = 1.45-1.90, p &lt; .001) and AAs are more likely to enlist (OR = 1.27, 95% CI =1.09-1.47, p &lt; .01) compared to other races. Furthermore, when examining just AAs, those with military service have an increased likelihood of developing DM (OR = 1.24, 95% CI = 1.12-1.38). However, 2009-10 data show current active duty and those who served within one year may experience some protective effects; when controlling for age, they have the same or decreased DM compared to other veterans or civilians.
Discussion: Military service does not buffer the risk for DM in AAs with high ACE, regardless of education. This may be partially due to an increase in ACE among those with a history of military service and the inclusion of the Veteran Administration population, which tend to have increase co-morbidities.

Disclosure
J.L. Wardian: None. E. Stone: None.
1578-P: Differences in Gut Microbiota between Diabetic and Nondiabetic Subjects
Authors: MURABAYASHI MASAYA , DAIMON MAKOTO , MURAKAMI HIROSHI , MATSUHASHI YUKI , KAMBA AYA , MIZUSHIRI SATORU 
Published: June 2020

Objective: Since gut microbiota activity results in the production of specific microbial-derived metabolites (short-chain fatty acids) and structural components (LPS and peptidoglycans), changes in such microbiota composition appears to be involved in pathophysiology of common chronic disorders such as diabetes. Indeed, differences in gut microbiota between diabetic and nondiabetic subjects have been reported frequently in many ethnic groups, but not frequently in Japanese populations.
Research Design and Methods: Among participants of the population-based Iwaki study of Japanese held in 2016, those with information of gut microbiota evaluated using 16S rRNA sequencing were included in the study (gender (M/F): 446/672; age: 54.8±15.6). Diabetic (n=122): either on treatment for diabetes or HbA1c levels ≥6.5% or FBG ≥126 mg/dl; Nondiabetic (n=996): others.
Results: When examined the differences in gut microbiota in taxonomy category genus, proportions of several genuses were different between diabetic and nondiabetic subjects. Especially, proportions of the genus Streptococcus (4.79±9.53% vs. 2.12±3.74%, p=0.011) and Lactobacillus (3.11±8.10% vs. 0.344±1.95%, p &lt;0.01), which are known to produce lactic acid, were higher, and genus Blautia (5.52±4.26% vs. 7.31±4.37%, p &lt;0.01) and Bacteroides (8.14±9.38% vs. 10.8±7.84%, p &lt;0.01), which are known to produce short chain fatty acids, were lower in diabetics subjects than those in nondiabetic subjects.
Conclusions: Differences in gut microbiota proportion were also found between diabetic and nondiabetic subjects in a Japanese population. Since lactic acid is known to have an effect of suppressing pathogenic bacteria, the increase in lactic acid producing bacteria may have effects against infection. Further, since short chain fatty acids act as signal transduction molecules that affect energy metabolism, the decrease in short-chain fatty acid producing bacteria may be involved in the development of diabetes.

Disclosure
M. Murabayashi: None. M. Daimon: None. H. Murakami: None. Y. Matsuhashi: None. A. Kamba: None. S. Mizushiri: None.
1579-P: Prevalence and Incidence of Obesity and Diabetes in the United States, 2011-2017
Authors: NIELSEN JANNIE , GREGG ED , VENKAT NARAYAN K.M. , CUNNINGHAM SOLVEIG A. 
Published: June 2020

Recent studies in the U.S. have shown that incidence of diagnosed diabetes decreased and prevalence plateaued after 2010 despite a modest increase in obesity prevalence. If the reduction in diabetes incidence is driven by changing obesity levels, prevalence may be an inadequate metric. Therefore, we investigated changes in prevalence and incidence of obesity and diabetes in adults in the United States during a 17 year period, 2001-2017.
We analyzed adult (≥20years) anthropometric records and diabetes status (prediabetes and diabetes combined) from the U.S. national representative Panel Study of Income Dynamics, 2001-2017. We used Poisson models to estimate the prevalence biannually from 2001-2017 and incidence of obesity (cases/1000 person years, PY) over 4 periods (2001-2005; 2005-2009; 2009-2013; 2013-2017). All rates were age- and sex-adjusted; survey weights were applied.
Prevalence of obesity increased from 20.9% (95% CI, 19.4-22.5) in 2001 to 26.4 (95% CI, 25.0-27.8) in 2009 to 29.5% (95% CI, 28.1-31.0) in 2017 (p&lt;0.001), while prevalence of diabetes over the same period increased from 7.5% (95% CI, 6.6-8.4) to 10.0 (95% CI, 9.0-11.0) to 11.8% (95% CI, 10.8-12.7) (p&lt;0.001).
The incidence of obesity changed over time: Compared to an obesity incidence of 29.3/1000 PY (95% CI, 26.0-35.5) in 2001-2005, the incidence increased to 31.7/1000 PY (95% CI,29.7-33.7) in 2005-2009, decreased to 28.8/1000 PY (26.3-31.4) in 2009-2013, and increased to 34.0/1000 PY (95% CI, 31.1-36.7) in 2013-2017 (p&lt;0.001). There was an overall linear increase in diabetes incidence across periods from 2001-2005 to 2013-2017 (p&lt;0.001): In 2001-2005, incidence was 11.9 per 1000 PY (95% CI, 10.6-13.1); in 2005-2009, 12.4/1000 PY (10.7-14.2); in 2009-2013, 13.3/1000 PY (11.5-15.2); and in 2013-2017 14.0/1000 PY (95% CI, 12.2-15.8).
In contrast to other U.S. studies of diabetes incidence, our results show no decline during the 2010s. This may be explained by our findings of an increasing incidence of obesity in the same population.

Disclosure
J. NIelsen: None. E. Gregg: None. K. Narayan: None. S.A. Cunningham: None.

Funding
Independent Research Fund Denmark (DFF-7025-00053B); National Institute of Diabetes and Digestive and Kidney Diseases (P30DK111024, 1R01DK115937)
158-LB: iSpy: A Pilot Study of a Novel Carbohydrate Counting Smartphone App for Youth with Type 1 Diabetes
Authors: CHOI ELIZABETH , ALFONSI JEFFREY , ARSHAD TAHA , SAMMOTT STACIE-ANN , PAIS VANITA , STINSON JENNIFER , PALMERT MARK 
Published: June 2020

Introduction: Carbohydrate counting is an important part of diabetes management, but it is difficult and can negatively impact quality of life. To address this challenge, a novel mobile application (app) called iSpy that uses computer vision and artificial intelligence to identify foods, estimate their carbohydrate content, and provide real-time feedback to users, was developed.
Objective: To evaluate iSpy’s functionality among youth aged 10 to 17 years (mean age = 14 years) with type 1 diabetes mellitus (T1DM).
Design/Methods: A pilot study among 44 youth (22 each randomized to intervention (iSpy) or control) was conducted to assess: (1) whether iSpy leads to improved carbohydrate counting ability (accuracy and speed) measured using standardized meal plates at baseline and at the end of the 12-week study; and (2) implementation outcomes such as app acceptability and technical problems.
Results: Compared to the control group, the intervention group improved its carbohydrate counting accuracy (total grams per meal) over the 12-week study period (p=0.007). Moreover, the percentage of times the carbohydrate estimate of a particular item was off by more than 10g from the true value was reduced by 9.9% (p=0.047). There was no statistically significant difference in total time to count carbohydrates. Qualitative interviews and app acceptability scale scores across 7 domains were primarily positive. 43% of iSpy participants were still using the app and logged at least once per 2 weeks at the end of the study. No major technical challenges were identified.
Conclusion(s): Participants using iSpy demonstrated improved carbohydrate counting accuracy and positive acceptability. iSpy has the potential to improve diabetes management, which should be further tested in a formal randomized controlled trial.

Disclosure
E. Choi: Other Relationship; Self; Inner Analytics Inc. J. Alfonsi: Other Relationship; Self; Inner Analytics Inc. T. Arshad: None. S. Sammott: None. V. Pais: None. J. Stinson: None. M. Palmert: None.

Funding
Physician Services Incorporated Foundation
1580-P: Hemoglobin A1c (HbA1c) Trajectories of Youth with Type 1 Diabetes (T1D) over 10 Years Postdiagnosis from Three Continents
Authors: SHERR JENNIFER , SCHWANDT ANKE , PHELAN HELEN , CLEMENTS MARK A. , HOLL REINHARD W. , BENITEZ-AGUIRRE PAUL Z. , MILLER KELLEE , WOELFLE JOACHIM , DOVER THOMAS , MAAHS DAVID M. , FRÖHLICH-REITERER ELKE , CRAIG MARIA E. 
Published: June 2020

Distinct HbA1c trajectories have previously been identified in youth with established T1D. We used data from 3 international registries to evaluate whether distinct HbA1c trajectories occur from T1D onset. Participants were &lt;18 yrs old at diagnosis and had at least 1 HbA1c measured within 12 months post-diagnosis, along with ≥3 duration-year aggregated HbA1c values over 10 yrs of follow-up. 7,292 participants from the Australasian Diabetes Data Network, 39,226 from the German/Austrian Diabetes Prospective Follow-up, and 3,704 from the U.S. T1D Exchange were included. Group-based modelling identified unique HbA1c trajectories from onset of T1D. Five distinct trajectories occurred in all 3 registries (Figure), with similar patterns of proportion by group. Over 50% had stable HbA1c in or near target. Conversely, ∼15% in each registry were characterized by stable HbA1c &gt;8.0%, and ∼11% had values that began in or near target but then increased. Only ∼5% of youth were suboptimal from diagnosis with an increasing HbA1c. This group differed from others with higher rates of minority status and older age at diagnosis across all 3 registries (p≤0.001). We observed similar post-diagnostic HbA1c patterns across 3 international registries. Identifying youth at greatest risk for deterioration in HbA1c over time may allow clinicians to intervene early to avert increasing HbA1c.

Disclosure
J. Sherr: Advisory Panel; Self; Bigfoot Biomedical, Cecelia Health. Consultant; Self; Eli Lilly and Company, Lexicon Pharmaceuticals, Inc., Medtronic, Sanofi, T1D Exchange. A. Schwandt: None. H. Phelan: None. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc. R.W. Holl: None. P.Z. Benitez-Aguirre: None. K. Miller: None. J. Woelfle: Advisory Panel; Self; Novo Nordisk A/S. T. Dover: 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. E. Fröhlich-Reiterer: None. M.E. Craig: None.

Funding
German Centre for Diabetes Research (FKZ82DZD01402); German Diabetes Association; European Foundation for the Study of Diabetes; INNODIA; The Leona M. and Harry B. Helmsley Charitable Trust