bluuebunny/crossref_metadata_2025_split huggingface dataset:
1421-P: Neighborhood Vulnerability and Diabetes Prevention Program Referrals in a Bronx Health System
Authors: DIAZ KATELYN , STEPHENSON-HUNTER CARA , CHAMBERS EARLE 
Published: June 2024

Research on the determinants of Diabetes Prevention Programs (DPP) referral typically focuses on individual-level factors like patient and provider characteristics, neglecting neighborhood context. We aim to examine neighborhood environment vulnerability as a factor influencing likelihood of referral to a health system DPP in the Bronx, NY.
NEVI is a neighborhood vulnerability index based on composite measures reflecting 4 primary neighborhood-level domains (demographic, economic, residential, and health status) measured at the census tract-level. High and low NEVI was determined by a median split (median=0.48; range: 0.19-0.58). This study used an observational cross-sectional design and recorded patient demographic, anthropometric, and DPP referral data from the electronic health records. Eligibility was based on hemoglobin A1c (HbA1c) between 5.7-6.4 and body mass index (BMI) ≥25 (​​kg/m2), a NYC residential address, and seen for a PCP visit between January 2018 and February 2020. The association between NEVI and DPP referral was examined using logistic regression, adjusted for age, sex, insurance type, preferred language, HbA1c (mg/dL), and BMI.
Of 9,319 eligible patients, 66% were female, 78% were Hispanic or non-Hispanic Black, and the mean age was 53 years (SD: 13.9 years). Of referred patients, approximately 18% (n=851) have a higher NEVI while 12% (n=542) have a lower NEVI (p=2.2e-16). In the adjusted analysis, higher odds of referral were observed for patients residing in high NEVI neighborhoods than low NEVI neighborhoods (OR=1.50, 95% CI, 1.28-1.65).
We show a 50% higher likelihood of DPP referral among patients who reside in a high-vulnerability neighborhood. The reasons contributing referrals to DPP are likely multifactoral, including individual patient characteristics and PCP characteristics (e.g. knowledge & discretion). Our study highlights the importance of neighborhood-level context as a potential factor influencing PCP referrals to DPP.

Disclosure
K. Diaz: None. C. Stephenson-Hunter: None. E. Chambers: None.

Funding
National Institutes of Health (DK121896-04S1)
1425-P: Determinants of Cost-Related Medication Nonadherence in Adults with Diabetes in the United States, 2015 to 2018
Authors: WU CHIEH-CHEN , WENG YUNG-CHING , ISLAM MOHAIMENUL 
Published: June 2024

Introduction & Objectives: Cost-related medication nonadherence (CRN) can lead to serious consequences including diabetic complications and increased mortality. The aim of this study was to identify the factors influencing CRN among diabetic patients and assess their relative contributions in explaining the occurrence of CRN.
Methods: This study leveraged four years of nationally representative data in the United States with diabetes from the 2015 - 2018 National Health Interview Survey (NHIS). Adult participants (≥18 years) with skipped doses to save money, took less medication to save money, or delayed filling a prescription to save money were considered to have experienced CRN. Logistic regression was applied to identify factors independently associated with CRN.
Results: Of the 12990 adult individuals with diabetes, 9.96% (weighted 4.4 million) of those adults reported CRN. Lack of insurance (OR: 4.32, 95%CI:3.16 - 5.92), education (OR: 1.38, 95%CI: 1.03 - 1.84), current smoking (OR: 1.77, 95%CI:1.39 - 2.26), obesity (OR: 1.53, 95%CI:1.12 - 2.10), female sex (OR: 1.47, 95%CI: 1.21 - 1.79), asthma (OR:1.25, 95%CI: 1.05 - 1.48), and insulin use (OR: 1.45, 95%CI: 1.19 - 1.76) were associated with higher odds of CRN among adult patients with diabetes. Non-Hispanic Black individuals were less likely to be adherent compared with White individuals (OR: 1.07; 95% CI: 0.83 - 1.36).
Conclusion: This cross-sectional study reveals that 1 in 10 diabetic individuals report nonadherence due to cost-related issues. Addressing financial barriers and enhancing healthcare accessibility, especially in marginalized communities, can potentially mitigate CRN and improve overall health outcomes.

Disclosure
C. Wu: None. Y. Weng: None. M. Islam: None.
143-OR: A1C Change after Initiating Continuous Glucose Monitoring (CGM) for People with Diabetes in a Large U.S. Managed Health Care Plan
Authors: NORMAN GREGORY J. , FERNANDES JOAQUIM , NEMLEKAR POORVA , LUPTON LAURA , BERK ALEXANDRA 
Published: June 2024

Background: National guidelines stipulate lifestyle and glycemic management for diabetes care. However, diabetes control remains inadequate for many people. CGM devices can improve detection and awareness of dysglycemia. Examining glycemic changes related to real-world CGM use can inform clinical and policy decisions about implementing this technology.
Methods: This was a retrospective observational study of medical claims data from Aetna’s fully insured member population. The index date was the first claim for CGM from 1/1/2019 through 12/31/2021. A1c was measured in the 3m prior to the index date and during the follow-up period. Patients were classified as having T1D or T2D based on ICD10 codes.
Results: A cohort of 7,814 patients (25% T1D, mean age = 58, 43% Medicare insured, 46% female, 56% white) met the inclusion criteria. A subset (n = 1,127) had A1c measures at baseline and in the last 3m of follow-up. Across all patient groups, A1c at baseline was &gt; 8.0 with statistically significant reductions at end of follow-up (Table). Analysis of all available A1c measures indicated reductions occurred primarily in the first 3m of CGM initiation and were sustained over the 12m.
Conclusion: CGM was associated with clinically meaningful and sustained reductions in A1c for patients with T1D and T2D, suggesting a population approach to CGM use for all patients with diabetes may be warranted.

Disclosure
G.J. Norman: Employee; Dexcom, Inc. J. Fernandes: Employee; CVS Caremark. Research Support; Dexcom, Inc., Pfizer Inc., Novo Nordisk, Sage Pharmaceuticals, Becton, Dickinson and Company, Aetion, Inc., Boehringer-Ingelheim. P. Nemlekar: Employee; Dexcom, Inc. L. Lupton: Employee; CVS Health. A. Berk: Employee; CVS Caremark.
1432-P: Identifying Prediabetes Phenotypes and Their Associations with Clinical Outcomes—Findings from the Center for Cardiometabolic Risk Reduction in South Asia (CARRS) Study
Authors: JAGANNATHAN RAM , KONDAL DIMPLE , TIWARI PRADEEP K. , GUJRAL UNJALI , MOHAN ANJANA RANJIT , DEEPA MOHAN , WALIA GAGANDEEP K. , SENGUPTA SHANTANU , PANDEY RAJESH , RADHA VENKATESAN , ALI MOHAMMED K. , SUN YAN , PRABHAKARAN DORAIRAJ , TANDON NIKHIL , MOHAN VISWANATHAN , VENKAT NARAYAN K M 
Published: June 2024

Background: Current definitions of prediabetes fail to capture the heterogeneity of the type 2 diabetes (T2D) pathophysiology. We aim to identify clusters of prediabetes among South Asians and explore their associations with diabetes incidence and overall mortality.
Methods: Data from individuals with prediabetes based on the ADA criteria in the CARRS study were clustered using the k-means approach based on age, BMI, HbA1c, HOMA-B, and HOMA-IR. T2D incidence (based on the ADA criteria) and all-cause mortality were assessed using Cox proportional hazards models.
Results: Two distinct prediabetes phenotypes emerged: insulin resistant (35.8%) and insulin-deficient (64.2%). Among 5,794 individuals (normal glucose tolerance (NGT): 2,882; prediabetes: 2,912), over a median follow-up of 11 years, 487 individuals developed T2D, and 212 individuals died. Both insulin resistant and insulin deficient prediabetes phenotypes were associated with T2D incidence compared to NGT, but only insulin-deficient phenotype showed increased mortality risk (Table).
Conclusion: Two-thirds of South Asians with prediabetes exhibit insulin deficient phenotype, contributing to 74% of diabetes incidence and associated with increased mortality. This data could guide precision prevention interventions based on diabetes pathophysiology.

Disclosure
R. Jagannathan: None. D. Kondal: None. P.K. Tiwari: None. U. Gujral: None. R. Anjana: None. M. Deepa: None. G.K. Walia: None. S. Sengupta: None. R. Pandey: None. V. Radha: None. M.K. Ali: Advisory Panel; Eli Lilly and Company. Y. Sun: None. D. Prabhakaran: None. N. Tandon: None. V. Mohan: None. K. Narayan: None.

Funding
The CARRS study has been funded with federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health (HHSN2682009900026C, P01HL154996)
1433-P: Longitudinal Association between Smoking and Key Diabetes and Cardiovascular Biomarkers among American Indians with Diabetes
Authors: LI CHAO 
Published: June 2024

Introduction & Objective: American Indian (AIs) have been disproportionately burdened by diabetes. AI adults are twice as likely to have diabetes compared to Whites. However, there is limited research that has been focused on the longitudinal and quantitative relationship between smoking and diabetes among AIs. This study is aimed to evaluate the long-term relationship among smoking and key cardiovascular and diabetes biomarkers among AIs with diabetes.
Methods: This study uses existing data from the Strong Health Study (SHS), which is an on-going and multi-centered cohort study of cardiovascular disease and its risk factors among AIs. Original SHS participants with diabetes and who completed at least 1 follow up assessment are included in this study. The overall sample size is 1359. The exposure of interest is smoking, e.g., numbers of cigarettes smoked per day. The outcome variables are Hemoglobin A1C (HbA1c), fasting glucose (FG), 2-h glucose (2-h FG) and serum lipids including low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG) and total cholesterol (TC). Mixed models are performed to assess the longitudinal relationship.
Results: This study consists of 831 females and 528 males, and the average age is 57.6. Initial numbers of cigarettes smoked per day is positively associated with HbA1c (Relative Risk (RR)=1.02, p&lt;0.05) after adjusting for age, gender, education, marital status, diabetes treatment, physical activity, and waist hip ratio. Similar significant longitudinal associations are observed between numbers of cigarettes smoked per day and TG (RR=8.32, p&lt;0.05) and TC (RR=1.53, p&lt;0.05), and numbers of cigarettes smoked per day is negatively related with HDL (RR=0.84, p&lt;0.05) over time. There is no significant association between numbers of cigarettes smoked per day and FG and LDL levels.
Conclusion: Smoking has significant longitudinal adverse effects on HbA1c, TG, TC and HDL among AIs with diabetes.

Disclosure
C. Li: None.

Funding
American Diabetes Association (AWD-231969)
1437-P: Blood Pressure (BP) Trajectories and 32-Year Cardiovascular Disease (CVD) Incidence in Women and Men with Type 1 Diabetes (T1D)
Authors: MILLER RACHEL G. , ORCHARD TREVOR J. , COSTACOU TINA 
Published: June 2024

While women have lower BP compared to men, CVD risk has been shown to increase at lower BPs in T1D compared to the general population, raising the need to better understand sex-specific effects of BP on CVD risk in T1D. We thus examined associations between longitudinal systolic (SBP) and diastolic (DBP) BP trajectories and 32-year CVD incidence in the Pittsburgh Epidemiology of Diabetes Complications T1D cohort separately in women and men (n=300 and 304 free of CVD at 1986-88 baseline respectively, mean age 27, T1D duration 19 yrs). SBP and DBP were measured at baseline and 2, 4, 6, 8, 10, 18, 25, and 30 years’ follow-up (median 6 measures per person). Associations between longitudinal BPs and time to first CVD event, defined as the first instance of coronary artery disease or stroke, were assessed separately in women and men using joint models which estimate the longitudinal data process and time-to-event data process simultaneously. Models were adjusted for time-varying anti-hypertensive (AH) medication use status and baseline T1D duration, HbA1c, and other CVD risk factors (HbA1c, lipids, BMI, smoking, kidney markers). On average SBP was 5.3 mmHg lower (p&lt;0.0001) and DBP 6.1 mmHg lower (p&lt;0.0001) over follow-up in women vs. men. SBP increased similarly in women (0.48/yr) and men (0.46/yr) but DBP decreased faster in women (-0.28/yr vs. men -0.13/yr, p=0.02). AH rates were similar in men and women over time (p=0.25), increasing from ~10 to 70% over follow-up in both. After adjustment for time-varying AH use and baseline covariates, longitudinal SBP was similarly associated with CVD in both women (HR=1.03, p=0.001) and men (HR=1.03, p&lt;0.001) while longitudinal DBP had a slightly stronger association in women (HR=1.06, p=0.004; men: HR=1.04, p=0.04). Our results suggest that despite being significantly lower over time, BP remains an important CVD risk factor in women with T1D. Thus, further research into sex-specific BP goals is warranted to improve CVD risk reduction.

Disclosure
R.G. Miller: None. T.J. Orchard: None. T. Costacou: None.

Funding
American Diabetes Association (7-23-ICTSWH-19); NIDDK (R01DK034818)Rossi Memorial Fund (Pittsburgh, PA)
1453-P: Islet Autoantibody–Positive General Population Individuals Have Smaller Pancreas Volume Compared with Control Individuals
Authors: TRIOLO TAYLOR M. , VIROSTKO JOHN , WILLIAMS JONATHAN M. , BRONCUCIA HALI C. , HILMES MELISSA A. , REWERS MARIAN , MOORE DANIEL J. , POWERS ALVIN C. , STECK ANDREA , STUDY GROUP MAP-T1D 
Published: June 2024

Introduction & Objective: Patients with type 1 diabetes (T1D) have a decline in pancreatic volume (PV) after onset. Previous studies of PV prior to onset have focused on islet autoantibody positive (AAb+) first-degree relatives (FDR) of patients with T1D who have decreased PV. Nothing is known about PV in AAb+ individuals identified from the general population.
Methods: We analyzed 10 individuals who were identified through general population screening to be multiple AAb+, had a recent oral glucose tolerance test (OGTT) and MRI through the Multicenter Assessment of the Pancreas in T1D (MAP-T1D) study. We compared PV index (PVI) of AAb+ general population individuals to multiple AAb+ FDR (n=78; 40 Stage 1, 26 Stage 2, 12 unknown Stage), individuals with T1D (n=93), and control individuals without T1D (n=90). PVI is normalized for weight which accounts for potential confounding from age or sex.
Results: Most AAb+ general population individuals were male (8/10) and non-Hispanic white (9/10). Most (8/10) were Stage 1, and 2 were Stage 2 T1D. Age at MRI was 15.6 ± 2.8 years with a mean HbA1c 5.3 ± 0.3 %, fasting glucose 87 ± 5 mg/dl and 2-hour OGTT glucose 103 ± 32 mg/dl. AAb+ general population individuals had a lower PVI 0.75 ± 0.17 ml/kg compared to control individuals 0.96 ± 0.25 ml/kg (p=0.03). The PVI of AAb+ general population individuals was similar to that of AAb+ FDR 0.85 ± 0.26 ml/kg (p=0.21). Individuals with T1D had a lower PVI 0.60 ± 0.24 ml/kg than AAb+ FDR (p&lt;0.0001) but not AAb+ general population individuals (p=0.14).
Conclusion: AAb+ general population individuals and AAb+ FDR individuals have smaller PVI compared to control individuals without T1D. Individuals with T1D have a smaller PVI than AAb+ FDR. More study on pancreas size prior to T1D onset may help inform future prediction and prevention studies.

Disclosure
T.M. Triolo: None. J. Virostko: None. J.M. Williams: None. H.C. Broncucia: None. M.A. Hilmes: None. M. Rewers: Advisory Panel; Sanofi. Other Relationship; Sanofi. Consultant; Janssen Pharmaceuticals, Inc. Research Support; Juvenile Diabetes Research Foundation (JDRF). Consultant; Provention Bio, Inc. Research Support; Hemsley Charitable Trust, National Institute of Diabetes and Digestive and Kidney Diseases. D.J. Moore: None. A.C. Powers: None. A. Steck: None.

Funding
NIDDK K23 DK136931
1459-P: Risk Factors for Metabolic Syndrome in Adults with Type 1 Diabetes in a Chilean Cohort
Authors: ORREGO MAITE , GARCÍA CLAUDIA , TORO LUIS , VARGAS OSCAR , GOMEZ PATRICIA C. , CLAUDIA VILLARROEL ANA , VEGA EDITH , SANZANA MARIA G. 
Published: June 2024

Introduction: Risk factors associated with Metabolic Syndrome (MS) have been identified among patients with T1D, including obesity, insulin resistance (IR), family history of T2D, years since diagnosis, insulin total daily dose (TDD), and its method of administration. At a local setting, data on the impact of these variables are scarce.
Objective: To assess risk factors related to MS in adults with T1D from a Chilean cohort.
Methodology: Descriptive observational study using electronic health records from a tertiary hospital in Chile. Included were all T1D patients over 15 years with regular follow-ups between March 2020 and September 2023. Demographic, clinical, and laboratory variables were considered. MS prevalence followed the IDF 2005 definition (BMI ≥30 kg/m2 plus two other criteria). Statistical analysis using T-test and X2 depending on variables.
Results: A total of 44 patients were identified. The mean age was 42.9 (17-80) years, with 70.4% being female (n=31). The average BMI was 26.5±5.1 kg/m2, with 22.7% having a BMI ≥30 kg/m2. Insulin pump users were 38.6% (n=17, 15 on AID and 2 on PLGS). The mean A1C was 8.4±1.9%. Among the entire cohort, 6 patients (13.6%) met MS criteria, with arterial hypertension as the second most frequent criterion (83%). Besides weight and BMI, higher TDD and prandial insulin doses, and the presence of clinical signs of IR were identified as risk factors for MS (all with p&lt;0.01). A non-significant trend was observed in those with a family history of T2D (p=0.06) and TDD ≥0.6 IU/kg/day (p=0.08). No relationship was found with years since diagnosis or insulin administration method, although potential biases may exist.
Conclusion: The prevalence of MS in adults with T1D aligned with international reports. Higher TDD and prandial insulin doses, along with clinical signs of IR, were identified as risk factors for MS. These findings highlight the need for individualized preventive and management strategies for MS in T1D, ultimately improving overall health outcomes.

Disclosure
M. Orrego: None. C. García: None. L. Toro: None. O. Vargas: None. P.C. Gomez: None. A. Villarroel: None. E. Vega: None. M.G. Sanzana: None.
1462-P: Genetic Risk Score for Type 1 Diabetes across Ethnic Populations via Multitask Learning
Authors: LI MINGQIAN , LIN FENG , ZHANG CONGJING , VEHIK KENDRA , PARIKH HEMANG M. , ORAM RICHARD A. , QIAN XIAONING , HUANG SHUAI 
Published: June 2024

Introduction: Genetic risk scores (GRS) for the prognosis of Stage 3 type 1 diabetes (T1D) are potentially biased due to the predominant race/ethnicity (European) from which they were derived. We examined the performance of the current GRS2 model in The Environmental Determinants of Diabetes in the Young (TEDDY) study across different ethnic groups and proposed a fair GRS model using multi-task learning (MTL).
Methods: Through the lens of Multi-Task Learning (MTL), we incorporated different group lasso (GL) regularization strategies and have extracted commonly shared and different group-wise features among ethnic groups based on the identified single nucleotide polymorphisms (SNPs) set utilized by the published GRS2 model. Receiver operating characteristic area under the curve (ROC-AUC) is used to test performance by ethnicity.
Results: We studied 4,918 Europeans (ethnic majority), and 2,468 non-Europeans (ethnic minority) children; 274 ethnic majority and 116 ethnic minority children developed T1D. Our MTL-based strategy resulted in an improved GRS2 for each ethnic minority group. The ROC-AUC performance for the ethnic minority population demonstrated an improvement, increasing from 0.679 to 0.713, indicating a fairer prognosis performance compared to the 0.728 of the GRS2 for the ethnic majority population, which uses the 67 SNPs as features for different ethnic groups.
Conclusion: MTL with different regularization strategies for developing fair GRS models improved T1D prognosis performance for ethnic minority groups in TEDDY. Future efforts should aim to derive fair GRS models by considering fairness metrics when searching for predictive genetic markers.

Disclosure
M. Li: None. F. Lin: None. C. Zhang: None. K. Vehik: None. H.M. Parikh: None. R.A. Oram: Research Support; Randox R & D. Consultant; Provention Bio, Inc., Sanofi. X. Qian: None. S. Huang: None.
1463-P: Genetic Heterogeneity and Clinical Subtypes of Type 1 Diabetes
Authors: SHI YU , YUE TONG , TAN HUILING , DING YU , PAN LANXIN , GUO CHUANG , LUO SIHUI , ZHENG XUEYING , WENG JIANPING 
Published: June 2024

Introduction & Objective: Type 1 diabetes (T1D) is a heterogeneous disorder; however, the extent to which genetic and clinical factors distinguish T1D patient’s subgroups remains uncertain. We aimed to incorporate HLA alleles and their interactions and then develop a novel T1D Genetic Risk Score (GRS). Also, we utilized a phenome-wide association study (PheWAS) to refine T1D subtype classification.
Methods: Among 1,352 Chinese T1D cases and 1,602 healthy controls, we used logistic regression to identify HLA class I and II alleles associated with T1D risk and combined significant loci to create the GRS. We modeled interactions between alleles marking strongly associated HLA haplotypes. Then, we employed PheWAS to assess the associations between 43 phenotypes and risk genotypes.
Results: T1D patients had reduced polymorphisms in HLA alleles and attenuated linkage disequilibrium between alleles compared to controls. The HLA-DRB1*09:01-HLA-DQA1*03:02 loci were significantly associated with the risk of T1D. Additionally, HLA-B*58:01 and HLA-C*3:02 exhibited associations with T1D and linkage with the DR3 haplotype. We observed a stronger genetic risk associated with an earlier onset age, especially in childhood-onset patients (p&lt;0.001) based on the PheWAS and GRS. A significant association was found between lower ALT (p&lt;0.01) and higher GAD antibody titers (p&lt;0.001) and increased genetic risk in adult-onset patients.
Conclusion: Our study characterized the HLA type and the association of genetic risk in Chinese T1D patients with different ages of onset and autoantibody titers. We translated the T1D genetic and clinical subtypes to further our understanding of the etiology of these subtypes.

Disclosure
Y. Shi: None. T. Yue: None. H. Tan: None. Y. Ding: None. L. Pan: None. C. Guo: None. S. Luo: None. X. Zheng: None. J. Weng: None.