bluuebunny/crossref_metadata_2025_split huggingface dataset:
1596-P: Evaluating Reports of Pancreatic Malignancy and Liraglutide: A Pharmacovigilance Study of the FDA Adverse Event Reporting System
Authors: ALVAREZ CARLOS A. , TENG CHENGWEN , MANSI ISHAK 
Published: June 2020

Background: Animal studies have raised concerns about glucagon-like peptide-1 (GLP-1) receptor agonist usage and incidence of pancreatic premalignant changes. Although randomized controlled Trials (RCTs) have not described this association, smaller size and shorter duration of RCTs may limit their statistical power to find such an association. Liraglutide utilization has been increasing due to the prescribing label change and indication for cardiovascular disease.
Aim: Evaluate reporting associations between pancreatic cancer and liraglutide using FDA Adverse Event Reporting System (FAERS).
Methods: FAERS reports from 1 January 2004 to 31 December 2018 were included in the study. The Medical Dictionary for Regulatory Activities (MedDRA) was used to identify pancreatic cancer cases. Reporting odds ratios (RORs) and corresponding 95% confidence intervals (CIs) for the reporting associations among liraglutide, metformin, glyburide, glipizide, and empagliflozin and pancreatic cancer were calculated.
Results: A total of 10,015,622 reports were considered. Pancreatic cancer RORs (95% CI) in descending order were: Liraglutide 32.47 (30.53-34.52), metformin 6.85 (6.52-7.19), glyburide 5.24 (4.48-6.14), glipizide 3.83 (3.32-4.42), and empagliflozin 3.42 (2.50-4.67). The number of reports for pancreatic cancer increased from 2735 in 2010 to 4967 in 2018.
Conclusion: Liraglutide had greater reporting associations when compared to other antidiabetes medications. Further studies are needed to determine if this signal is causal.

Disclosure
C.A. Alvarez: None. C. Teng: None. I. Mansi: None.

Funding
National Institutes of Health (K08DK101602)
1599-P: Receipt of Comprehensive Diabetes Care in U.S. Adults with Diabetes: 2005-2016
Authors: SHIN JUNG-IM , WANG DAN , FERNANDES GAIL , DAYA NATALIE R. , GRAMS MORGAN , GOLDEN SHERITA , RAJPATHAK SWAPNIL , SELVIN ELIZABETH 
Published: June 2020

Background: Quality health care delivery is critical to prevent diabetes-related complications. However, there is limited national information about the extent to which a diverse population of U.S. adults with diabetes receive the American Diabetes Association (ADA) recommended comprehensive diabetes care.
Methods: We conducted a cross-sectional analysis of 4,114 adults aged ≥20 with self-reported diabetes from the National Health and Nutrition and Examination Survey (NHANES) 2005-2016. We estimated the prevalence and identified predictors of receipt of comprehensive diabetes care (i.e., meeting all 5 criteria in the past year: having a primary doctor for diabetes care and number of visits for this doctor ≥1, HbA1c testing, eye exam, foot exam, and cholesterol test).
Results: Among adults with diabetes, 10.9% (95% CI, 9.3-12.6%) had received comprehensive care in the past year (all 5 criteria). Overall, 71.7% (95% CI, 69.6-73.7%) met ≥3 criteria. In a fully adjusted model, those less likely to receive comprehensive care were current smokers, and adults with lower socioeconomic status, lack of insurance, or depression (Table).
Conclusions: Only 1 out of 10 U.S. adults with diabetes receive ADA recommended comprehensive diabetes care. Efforts are needed to improve health care delivery and equity in diabetes care. Insurance status and depression are important modifiable determinants of comprehensive care.

Disclosure
J. Shin: None. D. Wang: None. G. Fernandes: Employee; Spouse/Partner; Janssen Pharmaceuticals, Inc. Employee; Self; Merck Sharp & Dohme Corp. N.R. Daya: None. M. Grams: Other Relationship; Self; DCI. S. Golden: None. S. Rajpathak: Employee; Self; Menarini Group. E. Selvin: None.
16-OR: Codesigning an Intervention for Adults with New-Onset Type 1 Diabetes to Improve Psychological and Social Adaptation
Authors: DUE-CHRISTENSEN METTE , JOENSEN LENE E. , SARRE SOPHIE , WAD JULIE L. , ROMANCZUK EWA , FORDE RITA , WILLAING INGRID , FORBES ANGUS 
Published: June 2020

Adults newly diagnosed with type 1 diabetes (T1D) can experience significant psychosocial disruption, leading to maladaptive emotional and behavioral responses which may increase the risk of future complications. The aim of the study was to co-design an intervention with people with diabetes (PWD) and healthcare-professionals (HCPs) to address the psychosocial disruption associated with new onset T1D in adults. We used a co-design approach to stimulate the target populations to reflect on their experiences and generate ideas for the intervention. This involved the use of illustrations depicting common experiences of the time of diagnosis identified in a previous study among PWD and HCPs. Illustrations were used as conversation tools in six parallel workshops exclusively for PWD (n=24) and HCPs (n=55); and four workshops that brought PWD (n=31) and HCPs (n=19) together to prioritize intervention components. Data comprising of transcribed audio recordings and notes from participants were analyzed thematically with a view to intervention development. The workshops identified that the intervention should be phasic, with an initial focus on the psychosocial disruption of diagnosis, followed by the early experience of diabetes and how to adapt positively to a life with it. Participants constructed two integrated intervention components: 1) one-to-one sessions soon after diagnosis, with a HCP trained in using a psychologically modelled conversation tool addressing PWD’s experiences, thoughts and feelings about the diagnosis; and 2) a group-based intervention addressing common diabetes challenges, emotional issues and thinking traps, focusing on normalizing emotions and developing strategies for managing diabetes in day-to-day life. The co-design process revealed the benefit of adopting a bio-psycho-social perspective and introducing methods of support involving both HCPs and peers to activate positive adaptive strategies in adults with new onset T1D.

Disclosure
M. Due-Christensen: Research Support; Self; Novo Nordisk Foundation. Stock/Shareholder; Self; Novo Nordisk A/S. L.E. Joensen: None. S. Sarre: None. J.L. Wad: None. E. Romanczuk: None. R. Forde: None. I. Willaing: None. A. Forbes: None.

Funding
National Institute for Health Research (PB-PG-0317-20012); Novo Nordisk Foundation (NNF17OC0028274)
1609-P: Diabetes and Risk of All-Cause and Cancer-Specific Mortality in Breast, Prostate, and Colorectal Cancer Survivors
Authors: CHEN XIONG , XIAO JIAN , WANG YAFENG , SHEN FEIXIA 
Published: June 2020

Aims: To determine whether diabetes is associated with all-cause, cancer-related and cardiovascular disease (CVD) mortality and examine the effect of duration of diabetes on mortality in breast, prostate and colorectal cancer survivors.
Methods: National Health Interview Survey (NHIS) was used to select 37,993 adults cancer survivors from 1997 to 2013 linked data retrieved from the National Death Index through December 31, 2015. Cox proportional-hazards models were used to assess associations between diabetes and all-cause and cause-specific mortality.
Results: Over a median follow-up period of 13 years, 2,346 all-cause, 698 cancer and 506 CVD deaths occurred. Among all cancer survivors, patients with diabetes had hazard ratios (HR) of 1.36 (95% CI=1.28-1.44) for all-cause mortality, 1.14 for cancer-specific mortality (95% CI=1.03-1.27), 1.36 for CVD mortality (95% CI=1.18-1.55), 17.18 for diabetes related mortality (95% CI=11.51-25.64) and 2.51 for kidney disease mortality (95% CI=1.65-3.82). For specific cancer sites, the HRs for all-cause mortality among survivors with diabetes diagnosed with breast cancer, prostate cancer and colorectal cancer. Diabetes increased the risk of cancer-specific mortality among colorectal cancer survivors compared to nondiabetic survivors. However, there is no the increasing risk in breast or prostate cancer survivors with diabetes. Diabetes was associated with higher risk of diabetes related mortality when compared to nondiabetic breast, prostate and colorectal cancer survivors. Cancer survivors with diabetes had similar risk of all-cause and CVD mortality during the second 5 years of diabetes and above 10 years of diabetes as compared to nondiabetic patients.
Conclusions: Diabetes increased the risk of all-cause mortality among breast, prostate, and colorectal cancer survivors. Greater attention on diabetes management is warranted in cancer survivors with diabetes.

Disclosure
X. Chen: None. J. Xiao: None. Y. Wang: None. F. Shen: None.

Funding
Natural Science Foundation of Zhejiang Province (LWY20H020001); China Scholarship Council (201908330235); Science and Technology Project of Wenzhou Science and Technology Bureau (Y20190125); First Affiliated Hospital of Wenzhou Medical University (FHY2019015)
1614-P: Targeting People with Unknown Diabetes in Health Insurance Databases through Artificial Intelligence
Authors: FUENTES SONSOLES , HRZIC ROK , KAB SOFIANE , HANEEF ROMANA , FOSSE-EDORH SANDRINE , COSSON EMMANUEL 
Published: June 2020

Background: The studies on unknown diabetes are usually based on small samples of population. Information from the entire population can be accessible through health insurance databases like the French National Health Data System (SNDS) comprising data on reimbursement of out-of-hospital dispensed health care and from public and private hospitals from the 66 million people living in France. The objective of our study was to develop an algorithm to identify unknown diabetes cases in the SNDS using artificial intelligence (AI).
Methods: Data from the Constances cohort were used to develop the algorithm since individual information recorded in self-administered questionnaire, medical examination and biological tests is linked with data from the SNDS. We applied a supervised machine learning method, involving eight steps. First, the reference database was selected (n=44,185 participants), after excluding known diabetes cases. Then, the unknown diabetes cases - fasting blood glucose ≥7 mmol/L- were identified as target positive (n = 655). The following steps were: codification of the SNDS variables, split the referenced database into training and testing databases, selection of variables and training, validation and selection of algorithms.
Results: Among the 3471 variables coded, 12 variables were selected based on their ability to discriminate the target: unknown diabetes cases versus no diabetes. The final algorithm is a logistic regression model based on the 5 most discriminating variables: age, sex and number of out-of-hospital reimbursements in the previous year for blood lipid profile tests, general practitioner consultations and blood glucose tests. The specificity, sensitivity and precision of the algorithm were 70%, 71% and 69%, respectively.
Conclusion: AI opens many perspectives in terms of diabetes prevention. Thus, unknown diabetes cases could be ascertained for development and evaluation of prevention policies at a national, regional or local level.

Disclosure
S. Fuentes: None. R. Hrzic: None. S. Kab: Employee; Spouse/Partner; Sanofi. R. Haneef: None. S. Fosse-Edorh: None. E. Cosson: None.
1622-P: Glycemic Outcomes among Rural Patients in the Type 1 Diabetes Exchange Registry, January 2016 to March 2018
Authors: GILL ARASHPREET K. , GOTHARD MICHAEL D. , EARLY KATHALEEN BRIGGS 
Published: June 2020

Rural Americans with type 1 diabetes face health disparities that can contribute to diabetes-related complications. This study sought to examine glycemic outcomes among rural participants in the Type 1 Diabetes (T1D) Exchange clinic registry between January 1, 2016 and March 31, 2018. Data were identified by rural status as defined by the Centers for Medicaid and Medicare, and stratified by age range, and eventually multivariate regression modeling was performed to isolate glycemic differences based on hemoglobin A1c (HbA1c) levels. A full multivariate regression model including all significant (p&lt;0.05 via two-sided testing) differential factors was determined with an additional indicator for rural status. Finally, the multivariate model was reduced using backwards elimination stepwise procedures until only significant (p&lt;0.05 via two-sided testing) factors were included. Mean HbA1c levels for all rural participants (n=1837) were significantly higher (mean 8.71% [SD 1.66%]) compared to the non-rural group (mean 8.48% [SD 1.63%]), p&lt;0.001. Rural children under age 13 (n=224) had a significantly higher average HbA1c (8.65% [1.17%]) compared to the non-rural group (8.45% [1.19%]), p=0.022. Rural adolescents between ages 13-18 (n=609) had a significantly higher average HbA1c (9.39% [1.73%]) than the non-rural group (9.14% [1.69%]), p&lt;0.001. Rural young adults between ages 18-26 had a higher average HbA1c (9.07% [1.77%]) than the non-rural group (8.88% [1.78%]), p=0.042. The average HbA1c among rural adults (n=589) over age 26 was not significantly different (7.72% [1.21%] than those of non-rural adults (7.76% [1.15%]), p=0.503. There is a clear pattern in glycemic outcome disparities even after adjustment for characteristic differences between rural and non-rural participants in the T1D Exchange, most strikingly among youth. The full extent of this disparity and the contributing factors need to be more thoroughly studied to provide effective solutions.

Disclosure
A.K. Gill: None. M.D. Gothard: None. K. Briggs Early: None.
1634-P: Gotta LADA? Identifying Differences in Adults with Latent Autoimmune Diabetes and Type 2 Diabetes to Improve Screening and Diagnosis
Authors: LEE MICHELLE N. 
Published: June 2020

Introduction: Latent autoimmune diabetes in Adults (LADA) represents up to 14% of adult diabetes. Compared to type 2 diabetes mellitus (T2DM) patients, LADA is believed to be diagnosed more acutely in younger patients with a lower body mass index (BMI), worse hemoglobin A1Cs (HbA1C), and lower triglycerides (TG). This study was done to examine for actual clinical differences between LADA and T2DM.
Methods: A retrospective case-control study was performed on adults with LADA and randomly matched with T2DM adults. Patients were matched based on gender and age (within 5 years) at time of diagnosis at an allocation ratio of 1:3 using the Military Health System Diabetes CarePoint Registry. Retrospective data were pulled at time of diagnosis to included ethnicity, BMI, HbA1C, lipids, TSH, the presence of symptoms, and medical histories. Univariate analyses were done for each variable followed by a multivariate analysis to determine the best overall model and control for confounding.
Results: In univariate analysis, BMI (P &lt; 0.001), TG (P = 0.003), and HDL (P = 0.009) were significant predictors of LADA compared to T2DM at time of diagnosis. None of the acute symptoms, autoimmune disorders, or other medical history was found to be statistically significant. In the multivariate model, Caucasians were more likely to be LADA (OR = 14.1; 95% CI, 1.34 to 146.2, P = 0.027). Low BMI and low TG levels were found to interact inversely (P = 0.049) with the odds of being a LADA. However, low BMI effect was only significant with TG levels under 350 mg/dl, and low TG effect was only significant in those with BMI &lt;28. The final model correctly classified 86% and 94.4% of the case-controls and controls.
Conclusion: Statistically significant differences were found between BMI, TG and HDL levels at time of diagnosis between LADA and T2DM adults, similar to those previously reported. Strongest differences were noted between BMI and TG levels, which may assist in diagnosis of LADA patients.

Disclosure
M.N. Lee: None.
1640-P: Common and Ethnic-Specific Molecular Networks and Drivers of Insulin Resistance in African and European Ancestry Individuals
Authors: XU PENG , WANG MINGHUI , SHARMA NEERAJ K. , COMEAU MARY , CIVELEK METE , LANGEFELD CARL D. , ZHANG BIN , DAS SWAPAN K. 
Published: June 2020

Our understanding of how genetic variants influence molecular regulatory networks in glucose responsive tissues, and in turn affect cellular processes related to insulin resistance (IR) in different ethnic groups, remains incomplete. We employ a network modelling approach to integrate measures of insulin sensitivity, transcriptomic data in the adipose and muscle tissues, and genetic data from African Americans (AAs) in the AAGMEx cohort (N=256). Data from European ancestry (EA) individuals from AREA (N=99) and METSIM (N=770) cohorts are used as contrast. Multiscale Embedded GEne coexpression Network Analysis (MEGENA) in AAs identifies 167 and 63 transcript subnetworks (modules) enriched for Matsuda index and SI-correlated genes in adipose and muscle, respectively. Among these IR-associated modules, 11 in adipose and 6 in muscle, are enriched for the cis-eSNP genes. Bayesian probabilistic causal networks (BN) are constructed and subsequently used to identify key network drivers. Integration of MEGENA and BN in AAs identifies 99 and 39 key regulators for IR-associated adipose and muscle modules, respectively. Among the IR-associated modules in AAs, 23 in adipose and 5 in muscle are strongly preserved in the respective networks in the EA cohorts. Top 3 IR-associated preserved adipose modules are enriched for immune system, mitochondria and hormonal response and these modules are potentially regulated by KYNU, LDHD and ENO1, respectively. We identify 50 and 24 IR-associated but AA-specific modules (not preserved in EAs) in adipose and muscle, respectively. Conversely, we find 28 IR-associated but EA-specific modules in adipose. Ethnic group specific and shared modules reveal novel molecular mechanisms for IR. Enrichment of cis-eSNP genes in the network neighborhoods of key drivers suggest that regulatory SNPs may configure the networks by modulating transcript expression in adipose and muscle tissues, and are likely causal determinants for IR.

Disclosure
P. Xu: None. M. Wang: None. N.K. Sharma: None. M. Comeau: None. M. Civelek: None. C.D. Langefeld: None. B. Zhang: None. S.K. Das: None.

Funding
National Institutes of Health (R01DK118243, R01DK090111)
1645-P: Polygenic Risk Score for Prediction of Complications in Men and Women with Type 2 Diabetes
Authors: TREMBLAY JOHANNE , ATTAOUA REDHA , HALOUI MOUNSIF , TAHIR RAMZAN , LONG CAROLE , HIZEL CANDAN , CHALMERS JOHN , HARRAP STEPHEN , WOODWARD MARK , HAMET PAVEL 
Published: June 2020

Introduction: We assessed the performance of our newly developed polygenic risk score (PRS) to predict microvascular and macrovascular complications of type 2 diabetes (T2D) in men and women. The PRS is composed of 600 common genomic variants associated to diabetes, cardiovascular and renal diseases and their key risk factors selected from summary statistics of meta-analyses of published genome-wide association studies performed in over 1.2 million of individuals. The performance of the polygenic model was assessed by c-statistics in 4098 genotyped participants of European descent of the ADVANCE trial (46.4% women) followed during a period of five years.
Methods: The logistic regression model that included the PRS adjusted for the principal component (PC1) of genetically determined ancestry, age at diagnosis and T2D duration, and treatment assignment, did not include any clinical or outcome data.
Results: The discrimination between cases (having a specific complication) from controls (free of this complication) at entry in ADVANCE had AUCs for microvascular complications of 0.63 (0.60-0.65) in men and 0.66 (0.63-0.69) in women, sex differences p = 0.07. AUCs for macrovascular complications were 0.56 (0.54-0.58) in men and 0.57 (0.54-0.61) in women; p = 0.41. The AUCs for prediction of incident cases, defined as having an outcome during the ADVANCE trial (free of outcome at baseline) compared to controls that did not have a specific outcome at any time during the study, were for microvascular events 0.66 (0.63-0.70) in men and 0.71 (0.66-0.77) in women; p = 0.15. AUCs for macrovascular events were 0.65 (0.62-0.68) and 0.72 (0.68-0.76) respectively, p = 0.01. AUC for prediction of cardiovascular death occurring during the trial was 0.71 (0.67-0.75) in men and 0.77 (0.72-0.82) in women; p = 0.04.
Conclusion: Our polygenic model demonstrated an overall better performance in women than in men and a better prediction capacity in individuals free of previous events in both sexes.

Disclosure
J. Tremblay: Research Support; Self; Servier. Stock/Shareholder; Self; OPTITHERA. R. Attaoua: None. M. Haloui: None. R. Tahir: None. C. Long: None. C. Hizel: None. J. Chalmers: None. S. Harrap: None. M. Woodward: Consultant; Self; Amgen, Kyowa Hakko Kirin Co., Ltd. P. Hamet: Research Support; Self; Servier. Stock/Shareholder; Self; OPTITHERA.

Funding
Genome Quebec; Canadian Institutes of Health Research; MEIE; CQDM; Opti Thera; Servier, Canada Research Chair in Predictive Genomics
1649-P: Effect of Fasting Insulin-Derived Gene Variant Clusters on Cardiometabolic Traits
Authors: SEVILLA MAGDALENA , UDLER MIRIAM , KIM HYUNKYUNG , HSU SARAH , MEIGS JAMES B. , MANNING ALISA 
Published: June 2020

Background: Fasting insulin concentrations reflect insulin resistance, which increase is associated with subsequent risk of heterogeneous cardiometabolic traits, including coronary artery disease (CAD), blood pressure (BP), estimated glomerular filtration rate (eGFR), BMI and lipid traits (TG, HDL, and LDL). We clustered insulin-associated genetic variants to define heterogeneous insulin resistance domains and tested whether these were associated with different cardiometabolic traits.
Methods: We studied subjects of European Ancestry with genetic and clinical data available (N=18,127) in the Partners Biobank. We applied a soft clustering to 56 genome-wide association study (GWAS) fasting insulin genetic variants and 25 fasting insulin-related traits. We generated weighted genetic risk scores (GRS) for each cluster (alleles were aligned in the direction of increased fasting insulin effect). Then we aimed to identify clinical consequences between the clusters and cardiometabolic outcomes including CAD, SBP, DBP, eGFR, BMI, TG, HDL, and LDL. We tested the hypothesis that different genetic insulin domains will be cross-sectionally associated with some or all of these cardiometabolic traits.
Results: We obtained three novel clusters of genetic variants. Each cluster was represented by a set of traits and a set of loci. The first represented VF and SAT, with loci near DLG2, FAM169B and, RGS12 genes, and its GRS was positive associated with CAD (p=0.004). The second cluster represents insulin sensitivity and lipid traits with loci near IRS1, ARRDC4 and, SNTG1, and its GRS was associated with lower TG (p=0.00001), and higher HDL(p=0.007). The last cluster represents proinsulin with loci near FABP2 and, PCAT5. No associations were found with this cluster.
Conclusion: These results reveal different genetic insulin domains indicating different mechanisms for cardiometabolic traits associated with fasting insulin.

Disclosure
M. Sevilla: None. M. Udler: None. H. Kim: None. S. Hsu: None. J.B. Meigs: Consultant; Self; Quest Diagnostics. A. Manning: None.