bluuebunny/crossref_metadata_2025_split huggingface dataset:
1403-P: Prescribing Trends of Glucose-Lowering Medications among Adult Kidney Transplant Recipients with Type 2 Diabetes—A U.S. Population-Based Cohort Study
Authors: HANSRIVIJIT PANUPONG , PATORNO ELISABETTA , WEXLER DEBORAH J. , ABDI REZA , PAIK JULIE M. 
Published: June 2024

Introduction: Type 2 diabetes (T2D) is a common comorbidity among kidney transplant recipients (KTR) in the U.S. To date, there are no recent studies describing the use of glucose-lowering medications (GLM) in KTR. Thus, we investigated the prescribing trends of GLM among adult KTR with T2D in the U.S.
Methods: Prevalence of GLM prescriptions in adult KTR with T2D from 2014-2023 are reported from two large U.S. insurance claims databases (Optum Clinformatics and MarketScan).
Results: We identified 64,611 adult KTR with T2D. The mean age was 60.2±12.1 years, and 61.2% were male. The mean number of GLM used was 2.8±4.2 per patient. Insulin was the most prescribed GLM, but the trend declined from 59.5% in 2014 to 48.9% in 2023. Similarly, sulfonylureas (SU) showed a decreasing trend over time, from 16.7% in 2014 to 9.7% in 2023. In contrast, newer GLM such as sodium glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1RA), steeply increased throughout our study period. SGLT2i use increased from 2.9% in 2020 to 11.2% in 2023 while that of GLP1RA increased from 2.3% in 2014 to 9.4% in 2021.
Conclusion: Insulin was the most prescribed GLM to KTR with T2D. However, the decline in insulin and SU was proportional to the rise in GLP1RA and off-label SGLT2i use. This trend follows with the timeline of clinical trials on SGLT2i and GLP1RA.

Disclosure
P. Hansrivijit: None. E. Patorno: Research Support; Boehringer-Ingelheim, Food and Drug Administration (FDA), National Institutes of Health, Patient-Centered Outcomes Research Institute. D.J. Wexler: Other Relationship; Novo Nordisk. R. Abdi: None. J.M. Paik: None.
1404-P: U.S. Rural–Urban Disparities in Obesity Mortality, 2000–2019
Authors: VARGHESE BEN T. , MIELKE MICHELLE , VELLA ADRIAN , BAILEY KENT R. , DUGANI SAGAR B. 
Published: June 2024

Introduction: Obesity prevalence is higher in US rural vs nonrural counties. The objective was to evaluate trends in obesity-related mortality by rurality.
Method: We used CDC WONDER data, 2000−2019, on obesity as a multiple cause of death (ie, top 20 contributor) among US residents aged ≥25 years. County rurality was based on 6 levels: large central metro (urban) to noncore (rural). We obtained annual rates of obesity mortality/100 000 persons (AOM) and reported AOM fold change (2018-2019 vs 2000-2001) and annual percent change (APC) based on linear regression of log(rates).
Result: The AOM increased across all rurality levels (Figure). The AOM fold change (APC) ranged from 2.4 (4.9%) in large central metro to 2.9 (6.0%) in noncore, with 22% higher APC in noncore vs large central metro counties. Furthermore, in noncore vs large central metro counties, the APC was 13.8% higher in men and 33.6% higher in women. In noncore vs large central metro counties, the APC was 40.8% higher in the 25-54 age group, 24.6% higher in the 55-74 age group, but 4.8% lower in the ≥75 age group. In noncore vs large central metro counties by region, the APC was 41.9% higher (West), 15.8% higher (South), 9.4% higher (Midwest), and 1.4% lower (Northeast).
Conclusion: In US adults, obesity-related mortality increased 2-3 fold with higher rates in noncore counties, with additional disparity by gender, age, and region. The increasing mortality and persistent disparities require evaluation.

Disclosure
B.T. Varghese: None. M. Mielke: Consultant; Biogen. Advisory Panel; Eisai Inc., Merck & Co., Inc. Consultant; Eli Lilly and Company. Advisory Panel; Roche Diagnostics. A. Vella: Research Support; Novo Nordisk. Consultant; Hanmi Pharm. Co., Ltd., Crinetics Pharmaceuticals, Inc. Advisory Panel; Rezolute, Inc. K.R. Bailey: None. S.B. Dugani: None.

Funding
National Institutes of Health (K23 MD106230)
1407-P: Gaps in the Initiation of Diabetes Drugs with Cardiovascular and Kidney Benefits by Medical Specialists
Authors: MCCOY ROZALINA G. , VANDERGRIFT JONATHAN L. , GRAY BRADLEY M. 
Published: June 2024

Introduction: Endocrinologists, cardiologists, and nephrologists commonly care for people with diabetes and coexisting cardiovascular disease (CVD), heart failure (HF), and chronic kidney disease (CKD). Guidelines recommend that GLP-1 receptor agonists (GLP-1RA) and SGLT2 inhibitors (SGLT2i) be prescribed in this context, but they remain broadly underutilized particularly among older adults.
Methods: In this retrospective cohort study using 100% sample of 2019 Medicare fee-for-service claims linked to American Board of Internal Medicine data files, we calculated the proportions of pharmacologically treated patients ≥66 years with diabetes and CVD, HF, or CKD treated by endocrinologist, nephrologist, or cardiologist and subsequently filled GLP-1RA or SGLT2i prescribed by one of these specialists. We used logistic regression to quantify associations with GLP-1RA/SGLT2i prescribing and both patient and physician characteristics.
Results: We identified 402,922/ 330,003/ 638,408 pharmacologically treated patients seen by 5,338/ 8,059/ 10,761 endocrinologists/ nephrologists/ cardiologists, respectively. Most endocrinologists prescribed diabetes drugs to their patients (73.7%) while nephrologists and cardiologists rarely did (6.3% and 4.0% respectively). Among patients they prescribed to, just 12.2% of nephrologist-, 13.9% of cardiologist-, and 26.6% of endocrinologist-treated patients filled a GLP-1RA or SGLT2i prescription prescribed by that specialist. Across specialties, GLP-1RA or SGLT2i use were lowest for older patients (10-25 percentage points less for ≥85 vs 66-69 year olds). Differences were also observed by race, dual Medicare/Medicaid eligibility, and physician age.
Conclusion: Nephrologists and cardiologists caring for people with diabetes rarely prescribed GLP-1RA and SGTL2i to patients who would benefit from them and may benefit from additional training and support to prescribe them, particularly to older adults.

Disclosure
R.G. McCoy: Research Support; American Diabetes Association. Other Relationship; American Diabetes Association. Consultant; Wolters Kluwer Health. Research Support; National Institutes of Health, Patient-Centered Outcomes Research Institute. Consultant; Yale New Haven Health System. J.L. Vandergrift: Employee; American Board of Internal Medicine. B.M. Gray: None.
1408-P: Biobank Scale Identification of Exposome Risk Factors in Cardiometabolic Disease and Their Mediators
Authors: PATEL CHIRAG , TANGIRALA SIVATEJA 
Published: June 2024

Cardiovascular disease and diabetes are influenced by potentially many exposures; however, traditional epidemiological studies examine but a few candidate factors at a time. Here, we demonstrate a pipeline for identifying exposures, made efficient by integrating genetic data at biobank scale. First, we conducted an Exposure-Wide Association Study (ExWAS) to systematically identify observational associations across 362 exposures in type 2 diabetes [T2D] and coronary artery disease [CAD] in participants of the UK Biobank. Causality estimates between exposure-disease phenotype pairs using bi-directional Two-Sample Mendelian Randomization [MR] across two large biobanks, UK Biobank and FinnGen, were computed. For CAD, we identified 172 (47.5%) FDR-significant exposure factors and for type 2 diabetes we identified 224 (61.9%) factors, finding 157 in both. We were able to deduce genetic-based causality between 14 (out of 172 ExWAS-identified) in CAD and 16 (out of 224) in T2D. Among the MR-validated associations for CAD, we report the interquartile range (IQR) of ExWAS and MR-derived odds ratios to be [0.742, 0.892] and [0.389, 0.711], respectively. For T2D, we report the IQR to be [0.658, 0.897] and [0.277, 0.669] for ExWAS and MR-derived odds ratios (OR), respectively. For example, we found having a college or university degree is causally inversely associated with CAD (MR beta estimate = -0.569, MR p-value = 3.51x10-5). However, we found low specificity (37.3 % for CAD and 26.3% for T2D) suggesting that most ExWAS-identified (or observational) associations are confounded. We find three MR-validated associations for T2D and one MR-validated association for CAD to be strongly mediated by causal risk factors (e.g., Body Mass Index[BMI]). Biobank samples, replete with clinical, exposure and genetic information enhance identification of exposures for metabolic disease and their mediators.

Disclosure
C. Patel: None. S. Tangirala: None.

Funding
National Institutes of Health (R01ES032470)
1429-P: Multimorbidity among Adults with Diabetes in India
Authors: MOHAN SAILESH , JARHYAN PRASHANT , DUBEY MANISHA , SRINIVASAPURA VENKATESHMURTHY NIKHIL , PRABHAKARAN DORAIRAJ 
Published: June 2024

Introduction: Limited information is available on the burden of multimorbidity among diabetics in India.
Methods: We conducted a cross-sectional study among 12284 adults aged ≥ 30 years, using multistage cluster random sampling, from rural and urban areas of North and South India. Trained interviewers administered a questionnaire to collect data on socio-demographics, lifestyle characteristics and collected blood samples. We defined diabetes, hypertension, and their control as per standard definitions; dyslipidemia as per ATP IV or on treatment; CKD as those on treatment or with urine albumin-creatinine ≥30 mg/g or eGFR &lt;60 ml/min/1.73 m2; central obesity as waist circumference ≥ 90 cm (men) and ≥ 80 cm (women); heart disease and stroke per self-reported physician diagnosis or treatment. Multivariable logistic regression was conducted to determine factors independently associated with multimorbidity.
Results: The age-adjusted prevalence of diabetes was [17.5%, 95% CI (16.9-18.2)], 56.1% (54.5-57.6) were aware and 86.8% (85.3-88.2) were taking antidiabetic treatment. The prevalence of multimorbidity was higher among diabetics [hypertension: 55.1% (53.1-57.2), central obesity: 72.9% (71.0-74.8), dyslipidemia: 74.4% (72.5-76.2), CKD: 8.8% (8.7-8.9), heart disease: 5.5% (4.6-6.5), stroke: 2.0% (1.5-2.9)] compared to nondiabetics [hypertension: 31.1% (30.1-34.7), central obesity: 53.2% (52.5-53.8), dyslipidemia: 51.2% (50.6-51.6), CKD: 5.9% (5.6-6.2), heart disease: 2.2% (2.0-2.4), stroke: 1.1% (1.0-1.2)]. Increasing age, poor education [adjusted odds ratio (aOR): 1.7 (1.1-2.6)], richest [aOR: 2.1 (1.2-3.5)] and urban residents [aOR: 1.5 (1.1-2.0)] were more likely to have at least one multimorbidity. Those taking anti-diabetic treatment were less likely to have multimorbidity [aOR: 0.7 (0.5-0.9)].
Conclusions: High levels of multimorbidity in India warrants implementation of effective health system interventions for integrated management of diabetes and multimorbidity to improve outcomes.

Disclosure
S. Mohan: None. P. Jarhyan: None. M. Dubey: None. N. Srinivasapura Venkateshmurthy: None. D. Prabhakaran: None.

Funding
An unrestricted grant under the Lilly NCD Partnership programme.
1430-P: Demographic and Socioeconomic Predictors of Poor Glycemic Control among Rural Americans without a Prior Diagnosis of Diabetes
Authors: YI HAESEUNG , QUINTERO ARIAS CAROLINA , FLORES TAMMY A. , MOTOLA HALEY L. , YI STELLA S. , KOZIATEK CHRISTIAN A. , DORAN KELLY , ANTHOPOLOS REBECCCA , THORPE LORNA , TRASANDE LEONARDO , LEE DAVID C. 
Published: June 2024

Introduction & Objective: Rural residents experience higher diabetes rates than their urban counterparts, which may be due to poverty or living in food deserts far from grocery stores. This study sought to identify risk factors for poor glycemic control among rural residents without a prior diagnosis of diabetes using data from a detailed health survey and food frequency questionnaire (FFQ).
Methods: We performed HbA1c testing among residents of Sullivan County, New York, a rural region known to have poor health outcomes and socioeconomic instability. We collected data on demographics, socioeconomic factors (income, education, unemployment, financial, food, and housing insecurity), disability, grocery accessibility (travel time to store), packaged food consumption (frequency of eating canned or plastic wrapped foods), and diet quality (based on healthy eating index scores) as possible predictors of poor glycemic control.
Results: Among 232 rural residents without a prior history of diabetes, 3 (1.3%) were found to have HbA1c ≥ 6.5%, consistent with previously undiagnosed diabetes, and 52 (22.4%) were in a prediabetes range (HbA1c: 5.7% to 6.4%). In the multivariable linear regression, statistically significant predictors of poor glycemic control among these rural residents included age (p&lt;0.01), male sex (p=0.03), Black race (p&lt;0.01), and food insecurity (p&lt;0.01).
Conclusion: In our geographically-focused analysis of risk factors for rural diabetes, we did not find that diet quality or distance to grocery store were significant risk factors for poor glycemic control. Among the socioeconomic indicators tested, food insecurity was the only statistically significant risk factor for diabetes identified. Our results highlight the need to investigate how food insecurity increases diabetes risk among rural Americans especially if not mediated by poor diet quality, but through some other mechanism.

Disclosure
H. Yi: None. C. Quintero Arias: None. T.A. Flores: None. H.L. Motola: None. S.S. Yi: None. C.A. Koziatek: None. K. Doran: None. R. Anthopolos: None. L. Thorpe: None. L. Trasande: None. D.C. Lee: None.

Funding
National Institutes of Diabetes and Digestive and Kidney Diseases (R01DK124400)
1435-P: Heterogeneous Associations of Socioeconomic Status with Metabolic Disease in Racial and Ethnic Subgroups in the United States
Authors: JANE CROMER SARA , BURNETT-BOWIE SHERRI-ANN M. , PATEL CHIRAG 
Published: June 2024

Introduction: Unfavorable socioeconomic status (SES) is associated with adverse health outcomes including metabolic disease. However, whether these associations are uniform across US racial and ethnic subpopulations is unknown.
Methods: We examined the race-stratified association of two SES indicators (educational attainment and income-to-poverty ratio) with type 2 diabetes (T2D) and obesity prevalence among adult participants in the National Health and Nutrition Examination Survey 1999-2018. We tested for multiplicative interactions between race or ethnicity and SES on disease prevalence, estimated the heterogeneity of odds ratios (I2 statistic), and tested whether the association between SES and T2D was mediated by BMI in racial and ethnic subgroups.
Results: Age-adjusted prevalence of T2D and obesity was higher among non-Hispanic Black, Mexican American, Other Hispanic, and Other/Multi-Racial participants and in those with lower SES. Higher educational attainment and income were independently associated with lower prevalence of T2D and obesity among non-Hispanic White and Asian participants, with smaller or even opposite associations observed among other racial and ethnic groups (e.g., higher income was associated with higher obesity prevalence for non-Hispanic Black participants). The association between SES and T2D was mediated through BMI in non-Hispanic White participants but not non-Hispanic Black, Mexican American, and Other Hispanic participants (29-53% v. 0-9% mediation).
Conclusion: The associations between SES and pathways to metabolic disease are heterogeneous or point in opposite directions depending on racial or ethnic group. To prevent biased estimates, research on socioeconomic health disparities should examine for heterogeneity between groups, particularly racial and ethnic groups, and examine mediation in a longitudinal setting.

Disclosure
S. Cromer: Other Relationship; Johnson & Johnson Medical Devices Companies. Advisory Panel; Alexion Pharmaceuticals, Inc. Other Relationship; Wolters Kluwer Health. S.M. Burnett-Bowie: Advisory Panel; Upliv Health. C. Patel: None.

Funding
American Diabetes Association (7-21-JDFM-005); NIEHS (R01ES032470)
1436-P: High Prevalence of Exocrine Pancreatic Dysfunction in Pediatric Subjects with Diabetes Mellitus
Authors: TATUM JONATHAN D. , HORNUNG LINDSEY , ELDER DEBORAH A. , SHAH AMY S. , ABU-EL-HAIJA MAISAM 
Published: June 2024

Introduction & Objective: This study aimed to describe the frequency of exocrine pancreatic disease in youth with diabetes mellitus (DM) to better understand the connection between diseases of the endocrine and exocrine pancreas.
Methods: Data was obtained from a single center database of DM patients excluding pre-DM and neonatal DM diagnosis. Patients were categorized as having “Other” DM if they had CFRD, MODY, or drug/chemical induced DM. All patients were then evaluated for exocrine pancreas disease, inclusive of pancreatitis or pancreatic insufficiency.
Results: Nine hundred ninety-one patients with DM diagnosis were included and 37/991 (4%) were diagnosed with pancreatic exocrine disease. 11/991 had pancreatitis before DM while 9/991 developed pancreatitis post DM. There were no differences in age, race/ethnicity and sex between DM youth with and without exocrine disease or between those with vs without pancreatitis. Patients with exocrine disease were more likely to have “Other” DM and a lower median hemoglobin A1c at DM diagnosis (6.8% IQR 5.6-9.2% vs 11.2%, 8.9-13.8%; p&lt;0.0001). Excluding those with T1D, those with exocrine disease were more likely to have received insulin therapy (8/37, 22% vs 11/954, 1.2%; p&lt;0.0001) than youth with DM and no exocrine problem. Patients with pancreatitis at any time relative to DM diagnosis were more likely to have “Other” DM and less likely to have T1D (p&lt;0.001), had a lower median A1c (8%, IQR 5.8-9.4% vs 11.2.%, IQR 8.9-13.8%; p=0.0004), and more likely to have received insulin therapy but not have T1D (6/20, 30% vs 11/954, 1.2%; p&lt;0.001).
Conclusions: The co-existence of exocrine and endocrine pancreatic disease occurred in 4% of DM patients. Pancreatitis occurred at a rate of 1% in patients with DM, rates higher than the general population (0.01%). Future studies are needed to determine the specific mechanisms involved in the connection between the endocrine and exocrine pancreas in children.

Disclosure
J.D. Tatum: None. L. Hornung: None. D.A. Elder: Research Support; Dexcom, Inc. A.S. Shah: None. M. Abu-El-Haija: None.
1439-P: Vitamin D Intake and Risk of Islet Autoimmunity (IA) and Type 1 Diabetes (T1D)
Authors: NORRIS JILL M. , CLASEN JOANNA L. , LIU XIANG , UUSITALO ULLA , ANDRÉN ARONSSON CARIN , HUMMEL SANDRA , YANG JIMIN , REWERS MARIAN , MCINDOE RICHARD , HAGOPIAN WILLIAM , ZIEGLER ANETTE-GABRIELE , LERNMARK ÅKE , TOPPARI JORMA , RICH STEPHEN S. , ONENGUT-GUMUSCU SUNA , PARIKH HEMANG M. , ERLUND IRIS A.M. , AKOLKAR BEENA , KRISCHER JEFFREY , VIRTANEN SUVI 
Published: June 2024

Objective: We studied if vitamin D intake was associated with risk of IA or progression to T1D and whether single nucleotide polymorphisms (SNP) in vitamin D metabolism genes modify the effect of intake on risk.
Methods: We followed 7,520 high risk children from birth until development of IA, defined as persistent GAD65 (GADA), insulin (IAA) and/or IA-2 autoantibodies, and until progression to T1D. Vitamin D intake from foods and supplements was estimated with 3 day food records collected quarterly in the first year and then semi-annually to age 10. We analyzed vitamin D per energy intake in time-dependent Cox proportional hazards models, adjusting for country, sex, T1D family history, HLA, energy, ancestry and seroconversion age (for progression analysis). We tested interactions between intake and 18 SNPs in GC, CYP2R1, CYP24A1, DHCR7 and VDR on risk of IA and progression to T1D using a false discovery rate of &lt;0.05 for significance.
Results: Higher vitamin D intake was associated with increased risk of GADA-first, only in those with 2 minor alleles in a SNP in CYP24A1 (which inactivates vitamin D) (Figure). Higher vitamin D intake at seroconversion was associated with decreased risk of progression from IA to T1D, only in those with no minor alleles in SNPs in GC (vitamin D binding protein gene) and VDR (vitamin D receptor gene).
Conclusion: Vitamin D intake may have differing effects on risk of IA and T1D, depending on variants in vitamin D genes.

Disclosure
J.M. Norris: None. J.L. Clasen: None. X. Liu: None. U. Uusitalo: None. C. Andrén Aronsson: None. S. Hummel: None. J. Yang: 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. R. McIndoe: None. W. Hagopian: Research Support; Janssen Pharmaceuticals, Inc., Provention Bio, Inc. Consultant; Sanofi-Aventis U.S., Randox R & D. A. Ziegler: None. Å. Lernmark: Advisory Panel; DiaMyd Medical AB, Stockholm, Sweden. J. Toppari: None. S.S. Rich: None. S. Onengut-Gumuscu: None. H.M. Parikh: None. I.A.M. Erlund: None. B. Akolkar: None. J. Krischer: None. S. Virtanen: None.

Funding
NIH NIDDK (U01 DK63829, U01 DK63861, U01 DK63821, U01 DK63865, U01 DK63863, U01 DK63836, U01 DK63790, UC4 DK63829, UC4 DK63861, UC4 DK63821, UC4 DK63865, UC4 DK63863, UC4 DK63836, UC4 DK95300, UC4 DK100238, UC4 DK106955, UC4 DK112243, UC4 DK117483, U01 DK124166, U01 DK128847, and Contract No. HHSN267200700014C)