bluuebunny/crossref_metadata_2025_split huggingface dataset:
86-OR: Erectile Dysfunction in Men with Prediabetes and Type 2 Diabetes: The Diabetes Prevention Program Outcomes Study (DPPOS)
Authors: BLAIR YOONI , DOHERTY LINDSAY , TEMPROSA MARINELLA , WESSELLS HUNTER , POP-BUSUI RODICA , GADDE KISHORE M. , SINGH PRACHI , SARMA ARUNA V. ,   DPP RESEARCH GROUP
Published: May 2022

Introduction: Erectile dysfunction (ED) is more common and less responsive to pharmacotherapy in men with T2D. Data in men with prediabetes (PreD) and risk profiles in those with T2D, are lacking. We determined the burden and risk factors for ED in men with PreD and with T2D enrolled in the DPPOS.
Methods: Between 1996-1999, 3,234 adults with PreD were randomized to intensive lifestyle intervention, metformin, or placebo in the DPP. In 2001, metformin was continued for those originally randomized with modified lifestyle intervention offered to all. In 2016-2017, 568 (88%) of 648 male participants completed the International Index of Erectile Function (IIEF) . ED was defined as having low or very low confidence getting and keeping an erection and/or ED medication use. Backwards selection multivariable logistic regression models including DPP treatment estimated odds of ED for covariates significant in bivariate analysis in men with persistent PreD and T2D.
Results: Overall, 218 (38%) men met ED criteria. Prevalence of ED was similar in men with PreD (41%) and T2D (37%) (p=0.4) . In all men, older age (p&lt;0.001) , non-white race (p&lt;0.001) and depression (defined by Beck Depression Inventory or antidepressant use) (p&lt;0.01) were associated with ED. Among men with PreD, those assigned to intensive lifestyle intervention, but not metformin, had decreased odds of ED compared with the placebo group (OR=0.26, 95%CI=0.09, 0.77) . Among men with T2D, metabolic syndrome (MetS) , defined by NCEP ATP III criteria, was associated with increased odds of ED (OR=1.72, 95%CI=1.05, 2.83) .
Conclusions: ED is highly prevalent in men with PreD and with T2D. In addition to demonstrated factors typically observed in the general population such as younger age and absence of depression, intensive lifestyle intervention was protective in men at increased risk for T2D. In men with T2D, ED was associated with MetS. Our findings suggest metabolic mechanisms contribute to the pathogenesis of ED in T2D.

Disclosure
Y.Blair: None. L.Doherty: None. M.Temprosa: None. H.Wessells: None. R.Pop-busui: Advisory Panel; Averitas Pharma, Inc., Boehringer Ingelheim International GmbH, Nevro Corp., Novo Nordisk, Reata Pharmaceuticals, Inc., Regenacy Pharmaceuticals, Inc. K.M.Gadde: Research Support; AstraZeneca, BioKier, Inc. P.Singh: None. A.V.Sarma: None. D.Research group: None.

Funding
National Institute of Diabetes and Digestive and Kidney Diseases (UDK048489, UDK048339, UDK048377, UDK048349, UDK048381, UDK048468, UDK048434, UDK048485, UDK048375, UDK048514, UDK048437, UDK048413, UDK048411, UDK048406, UDK048380, UDK048397, UDK048412, UDK048404, UDK048387, UDK048407, UDK048443, and UDK048400)
863-P: SGLT2i Use and the Risk of Nephrolithiasis in Patients with T2D
Authors: PAIK JULIE M. , TESFAYE HELEN , CURHAN GARY , MASTRORILLI JULIANNA M. , WEXLER DEBORAH J. , KIM SEOYOUNG C. , PATORNO ELISABETTA 
Published: May 2022

Diabetes is associated with an increased risk of kidney stones. Sodium-glucose cotransporter-2 inhibitors (SGLT2i) might lower the risk of nephrolithiasis, as they increase urinary flow. However, a prior meta-analysis was inconclusive, and only one Danish study reported a lower risk of kidney stones. We investigated the association between SGLT2i use and the risk of nephrolithiasis in routine practice. Using claims data from 2 private health plans and Medicare (2013-2020) , we identified 331,028 pairs of 1:1 propensity score-matched adults with type 2 diabetes (T2D) initiating an SGLT2i or a DPP4 inhibitor (DPP4i) , and 358,203 pairs initiating an SGLT2i or a GLP1 receptor agonist (GLP1RA) . Our primary outcome was nephrolithiasis diagnosed by ICD codes in the inpatient or outpatient setting. We estimated hazard ratios (HRs) , rate differences (RD) and 95% confidence intervals (CI) , adjusting for 57 baseline covariates in the PS model. Over a mean follow-up of ∼months on treatment, the risk of nephrolithiasis was lower in the SGLT2i group compared with the DPP4i group (HR 0.74 [95%CI 0.71-0.77]; RD 4.0 [95%CI, -4.6, -3.4]) or the GLP1RA group (HR 0.69 [95%CI 0.67-0.72]; RD -4.1 [95%CI, -4.7, -3.5]) (Table) . Secondary definitions of the outcome based on a hospital discharge diagnosis produced consistent results. In routine care, SGLT2i use was associated with a reduced risk of nephrolithiasis, compared to DPP4i or GLP1RA use in patients with T2D.

Disclosure
J.M.Paik: None. H.Tesfaye: None. G.Curhan: Employee; OM1, Inc, Other Relationship; UpToDate. J.M.Mastrorilli: None. D.J.Wexler: Other Relationship; Elsevier, Novo Nordisk, UpToDate. S.C.Kim: Research Support; AbbVie Inc., Bristol-Myers Squibb Company, Pfizer Inc., Roche Pharmaceuticals. E.Patorno: Research Support; Boehringer Ingelheim International GmbH, National Institutes of Health, Patient-Centered Outcomes Research Institute.
873-P: Machine-Learning Models to Predict CKD and HF in Type 2 Diabetes Patients
Authors: TSUBOTA HIROO , SUZUKI ATSUSHI , MAKINO MASAKI , KANDA EIICHIRO , YAJIMA TOSHITAKA , KIDANI YOKO , MORITA NARU , KANEMATA SATOMI 
Published: May 2022

Chronic kidney disease (CKD) and heart failure (HF) are the first and most frequent, and life-threatening comorbidities in type 2 diabetes (T2D) patients. Early intervention is important to prevent CKD and HF onset and to improve prognosis of these patients; however, the risk assessment of CKD and HF remains to be established. We aimed to build a machine learning model to predict the risk of CKD or HF onset in the early-stage T2D patients without a history of CKD or cardiovascular diseases (CVD) . We developed prediction models using light gradient boosting machine (LightGBM) , neural network, logistic regression, and Cox proportional hazards model. The model was derived and validated in a sample of 217,054 T2D patients aged ≥18 years without a history of CKD or CVD extracted from a Japanese hospital-based administrative claims data. The outcomes used for the prediction model were diagnosis of incident CKD or HF and hospitalization for CKD or HF in 1, 2, 3, and 5 years. Based on the importance, 60 characteristics and laboratory data were selected and used for models. LightGBM outperformed other models in all outcomes, with AUROCs 0.777 for diagnosis of incident CKD or HF, and 0.785 for hospitalization for CKD or HF in 5years. (Figure) The present study demonstrated successful development of prediction algorithms to support identifying T2D patients with high-risk of CKD or HF onset, which will contribute to improvement of patient prognosis.

Disclosure
H. Tsubota: Employee; AstraZeneca. A. Suzuki: Research Support; Chugai Pharmaceutical Co., Ltd., Ono Pharmaceutical Co., Ltd., Taisho Pharmaceutical Holdings Co., Ltd., Takeda Pharmaceutical Company Limited. Speaker's Bureau; Amgen K.K., Eli Lilly and Company, Novo Nordisk A/S. M. Makino: None. E. Kanda: None. T. Yajima: Employee; AstraZeneca. Y. Kidani: Employee; AstraZeneca. N. Morita: Employee; AstraZeneca.
884-P: Associations between Health Care Access/Utilization and Racial/Ethnic Disparities in Diabetes ABC Measures among U.S. Adults
Authors: TANG SHICHAO , ZHANG PING 
Published: May 2022

Racial/ethnic disparities exist for both poor ABC measures (A1C &gt; 9%, blood pressure (BP) &gt;= 160/100 mmHg, and non-HDL cholesterol &gt;= 160 mg/dL) and access to care or utilization measures (health insurance coverage, having &gt;= 4 doctor visits in past year, and having a routine place of health care) among U.S. adults with diabetes. Using 2011-2018 National Health and Nutrition Examination Survey, we examined the associations between health care access/utilization and racial/ethnic disparities in poor ABC measures. We estimated the marginal effect of race/ethnicity on each ABC measure by each access-to-care/utilization measure, respectively, using weighted multivariate logistic regressions. Poor ABC measures were more prevalent in non-Hispanic Black (NHB) (A1C: 19%, BP:13%, non-HDL cholesterol: 22%) and Hispanic (25%, 9%, 34%) than non-Hispanic White (NHW) (11%, 7%, 21%) people. Similar disparities were observed for poor access to care/utilization: NHB (uninsured:11%, &lt;4 visits:38%, no routine place for care:4%) , Hispanic (23%, 44%, 11%) , NHW (6%, 34%, 4%) . Effects of access to care/utilization on poor ABC measures varied by measures of both access-to-care/utilization and ABC control. Compared with NHW, having no insurance vs. having insurance was much more likely to be associated with poor A1C level in NHB (16% vs. 5%, p &lt; 0.05) and Hispanic populations (26% vs. 7%, p &lt; 0.01) . However, insurance status was not associated with racial/ethnic disparities on poor BP and cholesterol measures. Having &gt;= 4 doctor visits in the past year and a routine place for care were not associated with racial/ethnic disparities of any poor ABC measures. Closing the insurance coverage gap by increasing insured rate among racial/ethnic minorities could help reduce the racial/ethnic disparity in A1C level. Future studies to explore reasons why other access-to-care/utilization measures were not associated with racial/ethnic disparities in poor ABC measures may be beneficial.

Disclosure
S.Tang: None. P.Zhang: None.
888-P: A Model to Assess the Financial Feasibility of Telemedicine-Based Pediatric T1D Care in Value-Based Care and Fee-for-Service Settings
Authors: PEI RYAN LEONARD , DUPENLOUP PAUL , PRAHALAD PRIYA , JOHARI RAMESH , ADDALA ANANTA , ZAHARIEVA DESSI P. , MAAHS DAVID M. , SCHEINKER DAVID 
Published: May 2022

We previously examined a type 1 diabetes care model based on the use of a remote patient monitoring (RPM) tool that analyzes CGM data. The care model was associated with improved glucose management and reduced provider effort. We compare the financial feasibility of this care model in value-based care (VBC) and fee-for-service (FFS) settings.
We set parameters based on the current deployment of the care model. In the FFS setting, we estimated Medicare reimbursement rates and labor costs for in-person diabetes education. We compared these to costs in a VBC setting where providers make prospective payments for RPM services. We evaluated the financial impact of the care model across a variety of parameters (Fig) .
In a FFS setting, we estimated that in-person care incurs net costs of $1per patient. Under a VBC contract, we estimated that prospective payments are offset by reduced labor costs, incurring net costs of $65 per patient (i.e., 49% savings) . These results are sensitive to input parameters (Fig) .
We identified financially viable payment models for an algorithm-enabled telemedicine-based care model associated with improved T1D glucose management. The analysis may inform operational planning at the clinic level as well as reimbursement policy design.

Disclosure
R.Leonard pei: None. P.Dupenloup: None. P.Prahalad: None. R.Johari: None. A.Addala: None. D.P.Zaharieva: Research Support; Insulet Corporation, International Society for Pediatric and Adolescent Diabetes, Leona M. and Harry B. Helmsley Charitable Trust. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. D.Scheinker: None.

Funding
Helmsley Charitable Trust.ISPAD-JDRF Fellowship.R18DK122422.SDRC.LPCH Auxiliaries.The Stanford REDCap platform (http://redcap.stanford.edu) is developed and operated by Stanford Medicine Research IT team. The REDCap platform services at Stanford are subsidized by a) Stanford School of Medicine Research Office, and b) the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1 TR001085.Stanford Maternal and Child Health Research Institute.
892-P: Generating Intercorrelated Data for Simulation Samples in Diabetes Microsimulation Experiment
Authors: GUAN DAWEI , LEE JI-HYUN , LOU XIANGYANG , BIAN JIANG , GUO YI , GUO JINGCHUAN , SHAO HUI 
Published: May 2022

Traditional data generating processes (DGP) in simulation treat input variables as independently distributed. Ignoring the intercorrelation between variables would reduce the accuracy of the simulation. A common solution is to use the correlation coefficient between variables to correct the DGP. However, challenges exist when the correlated variables are from different distribution types. Existing solutions restrict the correlation coefficients to cope with these challenges, limiting their practicality. This study aimed to validate a novel algorithm to relax the above restrictions. We have identified 426 individuals with self-reported diabetes from the National Health and Nutrition Examination Survey (2013-2018) . We extracted the distribution parameters (mean and SD) and the correlation coefficient matrix of 18 variables often used to characterize diabetes population, including demographics, biomarkers, and disease histories. Our algorithm involves two steps: all parameters were first simulated as correlated numeric variables with normal distribution, and then converted into values that follow their corresponding distributions (e.g., Bernoulli) . Simulation precision was evaluated by comparing observed and simulated data. There were no significant differences in any variables between the observed and the simulated data (all p&gt; 0.05) . The mean (SE) absolute difference between observed and simulated correlation coefficients was 0.032 (0.002) . Among the 153 correlation coefficients, the differences between observed and simulated were less than 0.in 1 (77.8%) coefficients, between 0.and 0.1 in 30 (19.6%) coefficients, and higher than 0.1 in 4 (2.6%) coefficients. The precision levels were consistent across subgroups stratified by age or cardiovascular disease. Our algorithm, with relaxed restriction on intercorrelation, can accurately generate data close to the observed data based on distribution parameters and correlation coefficient matrix.

Disclosure
D.Guan: None. J.Lee: None. X.Lou: None. J.Bian: None. Y.Guo: None. J.Guo: None. H.Shao: Board Member; BRAVO4HEALTH, LLC.
91-LB: Glycemic and Metabolic Effects of Long-Acting GLP-1 in T1D
Authors: MOHANDAS DEENE , GAO CATHERINE , CALMA JAMIE , BASINA MARINA 
Published: May 2022

GLP-1 receptor agonists were studied as adjunct therapies in T1D. While short-acting GLP-1 agents are not approved for T1D due to concern of increased DKA risk, long-acting GLP-1 medications are commonly prescribed for T1D. Our study aims to evaluate glycemic and metabolic benefits of long-acting GLP-1 therapy in T1D and assess safety concerns.
We conducted a retrospective chart review of T1D patients on a long-acting GLP-1 for at least six months. Parameters collected were A1c, 14-day CGM data at each visit, and metabolic data. Data from two years prior to starting GLP-1, and six or more months after starting GLP-1 were included.
We identified 54 participants with T1D on a long-acting GLP-1 (Semaglutide, Dulaglutide, long acting Exenatide, Albiglutide) . Mean GLP-1 duration was 23.85 mo ± 15.46. We found a significant increase in time in range, and significant reductions in A1c, time in hyperglycemia, 14-day Blood Glucose SD, DKA hospitalization, and weight after starting a long-acting GLP-1. Time in hypoglycemia decreased non-significantly. Full data can be found in Table 1.As more data emerges on cardiovascular and renal benefits of long acting GLP-1 in type 2 Diabetes, there have been no reported outcomes in T1D. Our study is the first and the only one that demonstrates glycemic and metabolic benefits of this class in T1D, and safety of its use over 1.5-2 years. It represents real life experience. This data needs to be confirmed by prospective studies.

Disclosure
D. Mohandas: None. C. Gao: None. J. Calma: None. M. Basina: None.
911-P: Longitudinal Trends in CGM and Pump Use: Real-World Data from the T1D Exchange QI Collaborative
Authors: EBEKOZIEN OSAGIE , MUNGMODE ANN , MIYAZAKI BRIAN , ROMPICHERLA SAKETH , LYONS SARAH , ALWAZEER MOUHAMMAD , SARHIS JENNIFER , MEKHOUBAD ALLISON B. , DESIMONE MARISA , AKTURK HALIS K. 
Published: May 2022

Background: The T1D Exchange Quality Improvement Collaborative (T1DX-QI) is a learning network of over forty US type 1 diabetes clinics. Participating clinics share data for benchmarking, population health trends and monitoring quality improvement activities.
Method: T1DX-QI clinics complete an extensive data-mapping process to submit reports from clinic electronic medical records (EMR) system. The T1DX EMR database contains information for over 55,000 US patients and allows for robust population health analysis including percentage of patients using a continuous glucose monitor (CGM) or insulin pump. The percentage of patients using either device was calculated from 2016-2021.
Results: From 2016 to 2021, the percentage of patients using CGM increased from 59% to 72%. Over the same time period, the percentage of patients using pumps remained stable (median 54%) . For both devices, racial disparity persisted: there was a 27% and 31% difference in percentage of non-Hispanic White (NHW) and non-Hispanic Black (NHB) patients using CGM in 2016 (N = 17431) and 2021 (N = 20908) , respectively; in the same years, there was a 36% and 37% difference in percentage of NHW and NHB patients using a pump.
Conclusion: While there is robust increase in diabetes device uptake, inequities persist in access to these technologies.

Disclosure
O.Ebekozien: None. H.K.Akturk: Research Support; Dexcom, Inc., Eli Lilly and Company, REMD Biotherapeutics. A.Mungmode: None. B.Miyazaki: None. S.Rompicherla: None. S.Lyons: None. M.Alwazeer: None. J.Sarhis: None. A.B.Mekhoubad: None. M.Desimone: None.

Funding
The Leona M. and Harry B. Helmsley Charitable Trust
92-LB: Higher Adherence and Persistence for Dulaglutide Compared with Oral Semaglutide at Six-Months Follow-Up with U.S. Real-World Data
Authors: PACZKOWSKI ROSIRENE , HOOG MEREDITH , PELESHOK JENNIFER , YU MARIA , HUANG AHONG , LIMONE BRENDAN , MANJELIEVSKAIA JANNA 
Published: May 2022

A retrospective, observational analysis of administrative claims data from the IBM MarketScan Databases assessed adherence and persistence among GLP-1 RA naive adult patients with T2D newly initiating dulaglutide (DU) or oral semaglutide (OS) between Sept 2019 and Nov 2020.
Patients had continuous enrollment in the 6-mo pre-index period and 6-mo follow-up periods. The DU patients were propensity-score matched 1:1 to OS patients (6,166 pairs) . The median age was 54 years at index date, and 48% were female. At pre-index, mean aDCSI was 0.6 and 10% of patients had ASCVD. Most patients used oral anti-hyperglycemic medication (87%) ; a smaller proportion used insulin (15%) . The most common prescribing provider was primary care (62%) .
More DU patients were adherent (proportion days covered [PDC] ≥80%) (65% vs. 50%, p&lt;0.001) and had higher mean PDC vs. OS (0.78 vs. 0.68, p&lt;0.001) . More DU patients were persistent on therapy compared to OS patients (72% vs. 57% p&lt;0.001) and had longer mean duration of persistence (148 vs. 128, p&lt;0.001) . Among patients with ≥2 fills of their index drug, 73% of DU and 62% of OS patients were adherent.
During the 6-mo follow-up period, DU patients had significantly higher adherence and persistence compared to OS patients.

Disclosure
R. Paczkowski: Employee; Eli Lilly and Company. M. Hoog: Employee; Eli Lilly and Company, Stock/Shareholder; Abbott, Eli Lilly and Company. J. Peleshok: Employee; Eli Lilly and Company, Eli Lilly and Company. M. Yu: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. A. Huang: None. B. Limone: None. J. Manjelievskaia: Employee; IBM Watson Health, Other Relationship; Eli Lilly and Company.

Funding
Eli Lilly and Company
925-P: European Survey on Adults with Type 1 Diabetes (T1D) and Their Caregivers: Insights into Experience of T1D Management
Authors: KULZER BERNHARD , DOWN SU , SEIGNEZ ANTOINE , VIVES ALIZE , BONNEMAIRE MIREILLE , PENFORNIS ALFRED 
Published: May 2022

Background and Aims: T1D requires constant self-management and can substantially impact quality of life. We aimed to survey patient and caregiver experiences of T1D management.
Methods: Adult people with T1D (PwD) or their caregivers living in France, Germany, Italy, Spain, or the UK were invited to complete an online survey (July-August 2021) .
Results: Responders were 458 PwD (54% female, mean age 47.8 years, mean age at diagnosis 24.4 years) and 54 caregivers. For PwD, moderate to very high impacts of T1D on daily life included fatigue (77%) and anxiety (65%) . The top 2 other illnesses with significant impact on PwD’s lives were hypertension (29%) and depression (26%) . Many PwD (68%) would have liked more psychological support, mainly from healthcare professionals (HCPs) . Most caregivers (76%) would have liked support, mainly from HCPs, especially diabetologists (39%) , nutritionists (31%) and psychologists (28%) . Over half of responders felt they lack knowledge on T1D (53% PwD, 56% caregivers) . Only 57% of PwD and 39% of caregivers had participated in education programmes; of those &lt;30% of PwD/caregivers still regularly attended. There were regional differences in the proportion of PwD who had attended education programmes (Figure) , but not in the proportion who still were regularly attending.
Conclusion: In Europe, PwD and their caregivers require more support and education for T1D management.

Disclosure
B.Kulzer: Advisory Panel; Bayer AG, Bayer AG, Becton, Dickinson and Company, Becton, Dickinson and Company, Berlin-Chemie AG, Berlin-Chemie AG, Dexcom, Inc., Dexcom, Inc., Insulet Corporation, Novo Nordisk, Roche Diabetes Care, Sanofi, Sanofi-Aventis Deutschland GmbH, Research Support; Novo Nordisk, Roche Diabetes Care. S.Down: Advisory Panel; Viatris, Speaker's Bureau; Abbott Diabetes, AstraZeneca, Lilly Diabetes, Merck Sharp & Dohme Corp., Novo Nordisk, Sanofi, Viatris. A.Seignez: Employee; Else Care. A.Vives: Other Relationship; Sanofi. M.Bonnemaire: Employee; Sanofi. A.Penfornis: Board Member; Insulet Corporation, Medtronic, Mundipharma, Novo Nordisk, Sanofi, Speaker's Bureau; Glooko, Inc., Insulet Corporation, Lilly, Novo Nordisk, Ypsomed AG.

Funding
Sanofi