bluuebunny/crossref_metadata_2025_split huggingface dataset:
1299-P: Individualized Type 2 Diabetes Risk Prediction
Authors: JAEGER BYRON C. , WELLS BRIAN J. , STAFFORD JEANETTE M. , CASANOVA RAMON , DEMESIE YITBAREK , BANCKS MICHAEL 
Published: June 2024

Introduction and Objective: Current T2D risk prediction models do not include individualized preventive intervention effects. We developed an individualized T2D prediction model with differential treatment effects based on an individual’s fasting glucose (FG) level and body mass index (BMI).
Methods: We included 2640 participants from the Diabetes Prevention Program (DPP) randomized to placebo, metformin, or lifestyle arms. Using a 50/50 train/test split, we developed and validated an individualized Cox model predicting T2D risk at 3 years (3yr) with adjustment for sex, HbA1c, triglycerides, FG, BMI, intervention, and interactions between intervention and age, FG, and BMI. We repeated this process 100 times and took the mean concordance (C)-statistic of the model. We then fit the model to all available data and calculated counterfactual T2D risk for each intervention.
Results: Mean (standard deviation) age was 51 years (11) and 54% of participants were women. The mean C-statistic was 0.78 (95% confidence interval: 0.74, 0.81). The optimal intervention (lowest 3yr risk) was lifestyle for 86% of participants and metformin for the remaining 14% (table). If assigned to lifestyle, 3yr risk was 10% for those where lifestyle was optimal and 21% when metformin was optimal. When metformin was optimal, it was associated with 44% lower T2D risk than placebo and 29% lower than lifestyle.
Conclusion: Individualized T2D risk prediction may improve preventive strategies.

Disclosure
B.C. Jaeger: None. B.J. Wells: None. J.M. Stafford: None. R. Casanova: None. Y. Demesie: None. M. Bancks: None.

Funding
American Diabetes Association (11-22-ICTSPM-18)
13-PUB: Safety and Efficacy of Tirzepatide in Patients with Solid-Organ Transplant
Authors: DOWLATSHAHI SAMANEH , PATHAM BHARGAVI , KANSARA ABHISHEK , ROGERS ALEX W. , SADHU ARCHANA R. 
Published: June 2024

Objective: Preexisting diabetes mellitus (DM) and post-transplant diabetes mellitus (PTDM) impact outcomes post solid organ transplantation (SOT). Benefits of tirzepatide are well studied in DM but not in patients after SOT. This study describes safety and efficacy of tirzepatide in SOT.
Methods: Retrospective data was collected on 16 SOT patients with DM/PTDM on insulin, oral agents or both after initiation of tirzepatide for 6 months. [Table 1] Changes from baseline in A1C and weight (efficacy), and eGFR and tacrolimus levels (safety) were assessed.
Results: From baseline to 6 months, mean A1C reduced by 14.8% (-1.3%) and weight by 6.9% (-5.4 kg), but were not statistically significant. eGFR and tacrolimus levels were unchanged. [Table 2]
Conclusion: Tirzepatide may be a safe and effective drug in SOT patients. Larger studies are warranted.

Disclosure
S. Dowlatshahi: None. B. Patham: None. A. Kansara: None. A.W. Rogers: None. A.R. Sadhu: Advisory Panel; Abbott Diagnostics.
1300-P: Visceral Adipose Tissue Volume Is Increased in Severe Insulin Resistant Diabetes
Authors: ZAHARIA OANA P. , KUPRIYANOVA YULIYA , BOBROV PAVEL , SCHÖN MARTIN , MÖSER CLARA , TRINKS NINA , MENDEZ CARDENAS DANIA M. , TRENKAMP SANDRA , BÓDIS KÁLMÁN , SCHRAUWEN-HINDERLING VERA , WAGNER ROBERT , RODEN MICHAEL 
Published: June 2024

Introduction & Objective: The severe insulin resistant diabetes (SIRD) endotype associates with markedly higher risk of metabolic dysfunction associated steatotic liver disease (MASLD). However, it is not known if ectopic lipid deposition is also higher in skeletal muscle or adipose tissue compartments than in other endotypes.
Methods: Participants (n=697, known diabetes duration &lt;1 y) of the prospective German Diabetes Study (GDS) underwent 1H magnetic resonance spectroscopy for quantifying intramyocellular lipids (IMCL) in tibialis anterior muscle, intrahepatic lipids (IHL), visceral (VAT) and subcutaneous adipose tissue (SAT) volumes. The validated clustering algorithm used age, sex, BMI, HOMA-IR, HOMA-B, islet-directed autoantibodies.
Results: As expected, SIRD (n=40) had the highest IHL (11±5%) compared to mean IHL values of 2-6% in moderate obesity-related (MOD, n=215), moderate age-related (MARD, n=237) and severe autoimmune diabetes clusters (SAID, n=193; all p&lt;0.01). Interestingly, SIRD presented with higher VAT than MOD, MARD and SAID (6089±2705 vs 3260±2310, 3056±1963, 1524±1401 cm3) even after adjustment for BMI (all p&lt;0.05). Also, SAT was higher in SIRD than MARD (27879±6777 vs 17127±6218 cm3, p&lt;0.05), while differences to other subtypes lost statistical significance upon correction for BMI. There were no differences in IMCL between subtypes. Overall, VAT correlated with cardiovascular risk (Framingham, r=0.661, p&lt;0.05), whole-body insulin sensitivity derived from hyperinsulinemic euglycemic clamp (r=-0.537, p&lt;0.05) and with beta cell function during intravenous glucose tolerance test (r=0.379, p&lt;0.05). Within SIRD, VAT was further associated with fasting glycemia (r=0.121, p=0.05) and fasting insulin resistance (HOMA-IR; r=0.372, p&lt;0.05).
Conclusion: The increases in IHL and VAT underline their key role in the pathophysiology and progression of severely insulin resistant diabetes and shall help to refine subtyping approaches for precision diabetology.

Disclosure
O.P. Zaharia: None. Y. Kupriyanova: None. P. Bobrov: None. M. Schön: None. C. Möser: None. N. Trinks: None. D.M. Mendez Cardenas: None. S. Trenkamp: None. K. Bódis: None. V. Schrauwen-Hinderling: None. R. Wagner: Speaker's Bureau; Sanofi. Advisory Panel; Lilly Diabetes. Speaker's Bureau; Boehringer-Ingelheim, Novo Nordisk. M. Roden: Advisory Panel; Eli Lilly and Company. Research Support; Boehringer-Ingelheim. Advisory Panel; Novo Nordisk. Research Support; Novo Nordisk. Advisory Panel; TARGET PharmaSolutions, Inc. Speaker's Bureau; AstraZeneca.

Funding
EFSD Rising Star Award
1306-P: Type 2 Diabetes Screening in the 12 Months following Acute SARS-CoV-2 Infection—A Real-World Cohort Analysis
Authors: EKANAYAKE RUWANTHI N. , CHAKKALAKAL ROSETTE , ALI MOHAMMED K. , SAM VARGHESE JITHIN 
Published: June 2024

Introduction & Objective: SARS-CoV-2 infection is associated with new-onset Type 2 Diabetes (T2D) and higher pandemic healthcare utilization. We evaluated Hemoglobin A1c testing rates after SARS-CoV-2 infection to explore whether new T2D diagnoses may be a consequence of increased post-infection screening.
Methods: We used data from adults (≥18 years) without diagnosed T2D from the OneFlorida+ clinical research network, who formed an Exposed cohort with a positive SARS-CoV-2 test (n = 67,343) over March 2020 - January 2022, an Unexposed cohort with negative tests (n = 206,281) over the same period, and an age-sex matched Historical cohort (n = 73,791) over March 2018 - January 2020. Using a difference-in-difference framework and stratified by race-ethnicity, we reported excess HbA1c tests in the year post-SARS-CoV-2 infection relative to the year before infection.
Results: The analytic sample was 20.5% NH Black and 22.0% Hispanic with mean age of 50.9 years (SD: 19.2). Relative to the Exposed cohort (change per 1000 individuals: 89 [95%CI: 87, 90]), the Unexposed (173 [95% CI: 171, 175]) and Historical (154 [95%CI: 143, 165]) cohorts showed larger changes in HbA1c testing. Associations were similar across racial-ethnic groups except NH Other, Figure.
Conclusion: Higher rates of diabetes screening may not explain the previously observed increased T2DM incidence following SARS-CoV-2 infection.

Disclosure
R.N. Ekanayake: None. R. Chakkalakal: None. M.K. Ali: Advisory Panel; Eli Lilly and Company. J. Varghese: None.

Funding
National Institutes of Health (R01DK120814-05S1, P30DK111024)
1309-P: Diagnosing Glucose Intolerance in Patients Attending ER in an Acute Hospital in England
Authors: JUDE EDWARD B. , HEALD ADRIAN H. , ANDERSON SIMON G. 
Published: June 2024

Introduction and Objective: Type 2 diabetes (T2D) and pre-diabetes (pre-D) are associated with greater risk of cardiovascular disease and mortality. Therefore, early diagnosis of glucose intolerance is important. Patients attending hospital can be screened for diabetes to identify those who will benefit from early treatment and future risk reduction. The aim of this study was to screen people attending Emergency Room (ER), not known to have T2D.
Methods: 3425 individuals (1935 women) were screened for pre-D and T2D using HbA1c at presentation. Demographics, including ethnicity, were also recorded and the Finnish Diabetes Risk Score (FINDRISC) was calculated. Glucose intolerance was defined according to ADA criteria (pre-D: HbA1c 39-47 and T2D: ≥48mmol/mol).
Results: Normal glucose tolerance (NGT) was seen in 1967 people (58.5%), pre-D in 1190 (30.2%) and T2D in 263 (7.8%). Mean (SE) age was: NGT 50.3 (0.32), pre-D 57.4 (0.41) and T2D 57.4 (0.87) years, respectively (P&lt;0.0001). Mean HbA1c: NGT 34.1 (0.1), pre-D 41.5 (0.1), T2D 57.6 (0.3) mmol/mol, respectively (p&lt;0.001). Mean weight and BMI was: NGT 80.5 (0.5), pre-D 82.3 (0.6) and T2D 90.2 (1.6) kg, respectively (p&lt;0.0001) and 28.4 (0.2), 29.2 (0.2) and 31.6 (0.4), respectively (p=0.0001). In a multinomial univariable logistic regression model for each unit increase in FINDRISC score the RRR (95% CI) for pre-D was 1.09 (1.06, 1.11) and for T2D 1.16 (1.11, 1.21). On adjusting for age and sex the RRR was 1.09 (1.08, 1.11) and 1.16 (1.13, 1.20) for pre-D and T2D, respectively (p&lt;0.001). FINDRISC of &gt;20 increased risk for pre-D and T2D 14.2 and 15.4-fold, respectively. South Asians had higher rates of glucose intolerance compared to Caucasians (32.9% vs 22.1%).
Conclusion: Patients attending ER have a high incidence of unknown glucose intolerance. The FINDRSC score may be used to identify those at risk. Use of HbA1c in ER is a practical and effective way of identifying previously unknown pre-D and T2D, leading to prompt implementation of targeted treatment.

Disclosure
E.B. Jude: Speaker's Bureau; AstraZeneca. Research Support; Novo Nordisk. Speaker's Bureau; Lilly Diabetes. Research Support; Abbott. Speaker's Bureau; Sanofi. A.H. Heald: None. S.G. Anderson: None.
1315-P: Pre– and Post–Liver Transplant Diabetes (DM) and Prediabetes (PD), and the Impact on Transplant Outcomes—Results of a Large Single-Center Study
Authors: SCOPELLITI MILENA , VAN STRIJDONCK CATO , GEZICI GIZEM , CARRAI PAOLA , PETRUCCELLI STEFANIA , CATALANO GABRIELE , MARTINELLI CATERINA , BRONZONI JESSICA , PEZZATI DANIELE , TINCANI GIOVANNI , BALZANO EMANUELE , FEMIA FRANCESCA R. , DEL TESTA ROBERTA , PELLUNGRINI ROBERTO , PEDRESCHI DINO , GIANNOTTI FOSCA , MARCHETTI PIERO , GHINOLFI DAVIDE , DE SIMONE PAOLO , MARSELLI LORELLA 
Published: June 2024

Introduction & Objective: Discussion is still ongoing on the prevalence and characteristics of DM and PD (IFG and/or IGT) in patients before (pre-Tx) and after (post-Tx) liver transplantation (LT), and the impact on post-Tx outcomes.
Methods: In this single center study we evaluated 1,468 patients [(age: 56±9 yrs; M/F: 1107/361; BMI: 24.4±5.8 Kg/m²; family history of diabetes (FHD): 37%; FPG: 109±36 mg/dl; HbA1c: 35±12 mmol/mol); main indications for LT: 57.7% HCV/HBV-related cirrhosis/HCC, and 27.5% dysmetabolic/esotoxic cirrhosis/HCC)] being waitlisted for LT from brain dead donors.
Results: Based on pre-Tx history, FPG, HbA1c and/or OGTT, 32.5% patients had DM and 21.4% PD, with the remaining 46.1% being normoglycemic (NG). DM and PD patients were older and predominantly males (both p&lt;0.001). Furthermore, DM subjects had higher BMI and FHD than PD and NG (both p&lt;0.050). From this cohort, 1,086 subjects underwent LT, 470 of which reaching a 5-year follow-up. In these latter, patient and graft survival was respectively 84.5% and 84.1%, and the prevalence of DM and PD increased respectively to 49% (p&lt;0.001) and 29% (p=0.053) vs Pre-Tx. To identify pre-Tx factors in NG associated with post-Tx DM, machine learning algorithms (both interpretable and explainable) were used (i.e. decision trees, explainable boosting machines and CatBoost) to assess multivariate correlations. Inspection of the models indicated that pre-Tx FPG, BMI, smoking and eGFR were some of the main factors correlated with post-Tx DM. In addition, duration of pre-Tx DM (p=0.051) and HbA1c (p=0.094) tended to impact on patient survival and/or graft outcome.
Conclusion: This accurate assessment of glycemic status of subjects waitlisted for LT implements previous available data and demonstrates a very high prevalence of DM and PD (&gt; 50%); it also identifies modifiable factors to possibly prevent post-Tx DM; duration of pre-Tx DM and its control could affect post-Tx outcomes.

Disclosure
M. Scopelliti: None. C. Van Strijdonck: None. G. Gezici: None. P. Carrai: None. S. Petruccelli: None. G. Catalano: None. C. Martinelli: None. J. Bronzoni: None. D. Pezzati: None. G. Tincani: None. E. Balzano: None. F.R. Femia: None. R. Del Testa: None. R. Pellungrini: None. D. Pedreschi: None. F. Giannotti: None. P. Marchetti: Speaker's Bureau; Eli Lilly and Company, Novo Nordisk. D. Ghinolfi: None. P. De Simone: None. L. Marselli: None.

Funding
PNRR Project - M4C2-I1.3 PE_00000019 HEAL ITALIA
1316-P: Integration of HCV Screening and Treatment in Patients with Diabetes Mellitus—A Multicenter, Prospective, Quality Improvement Program in Taiwan
Authors: OU HORNG-YIH , LIU CHUN-JEN , WANG CHIH-YUAN 
Published: June 2024

Objective: Patients with diabetes exhibit a higher incidence of Hepatitis C virus (HCV) infection, making them a crucial population for targeted HCV screening and micro-elimination. In light of the first published interdisciplinary consensus on managing/screening HCV in diabetic patients, this study aims to evaluate the results and identify effective strategies for incorporating HCV screening and treatment into an existing diabetes care model managed by case managers.
Methods: This is a multicenter prospective program initiated by a diabetes society. Eighteen centers in Taiwan developed strategies to enhance HCV screening and streamline linkage to HCV care in diabetic patients. The performance of each center was assessed based on the number of patients screened and treated with direct-acting antivirals (DAA), HCV micro-elimination achievement, and strategies novelty. The strategies of the top five centers were compared with others to identify key drivers for enhanced HCV screening and care.
Results: Over a median time of 6.1 months, 28,436 patients were screened, and 1,379 (5%) were anti-HCV positive. A total of 333 (22%) patients were positive for HCV RNA, among whom 288 (86%) received DAA. Overall, 27 strategies were adopted, with the following being the most effective: multidisciplinary alignment meetings, incorporating HCV screening into the hospital’s key performance index, nondiabetes/gastroenterology physician participation, physician-assisted screening, automated HCV test slip printing, an alert system for positive anti-HCV result, and fast track to specialist appointments.
Conclusions: This program pioneered the evaluation of the integration of HCV screening and treatment in diabetic patients, providing strategic insights into HCV micro-elimination through efficient patient identification and collaborative HCV management.

Disclosure
H. Ou: Speaker's Bureau; Abbott, AstraZeneca, Boehringer-Ingelheim, Sanofi, Eli Lilly and Company, Takeda Pharmaceutical Company Limited. C. Liu: None. C. Wang: None.
1322-P: Combination of LiverSTAT (LST) and Fibroscan (LSM) Outperforms FIB-4 and LSM for MASLD Advanced Fibrosis (AF) F3F4
Authors: ALKHOURI NAIM , NADEEM RIDA , LEFF PHILLIP , MANTRY PARVEZ , QUIAMBAO RONALD , DUPUY JULIE , MUNTEANU MONA , DE LEDINGHEN VICTOR 
Published: June 2024

Background: LiverSTAT (LST) is a new blood test for MASLD stratification.
Aims: To retrospectively compare the efficacy of two combinations in one-step approach with two combinations: LST&LSM versus FIB-4&LSM, for the identification of advanced fibrosis (F3F4) in a multicenter multiethnic meta-dataset of MASLD patients.
Methods: Retrospective data from 5 hepatology centers from MASLD patients that underwent liver biopsy (LB) along with LSM, FIB-4 and LST. Efficiency has been assessed using concordance rates, number needed to screen (NNS) on subject with LB F3F4.
Results: Data from 786 patients (22%US, 39%Asia, 39%EU) was analyzed [age 57.1years, female 54.5%, BMI 31.4, ALT 52U/L]. LB prevalence of F3F4 was 32.8%.In the overall cohort, LB confirmed F3F4 among concordant LST&LSM and FIB-4&LSM, respectively, in 107/142(75%) vs 54/64(84.4%) with a screening efficiency (NNS) of 1.8 vs 2.2; the combination LST&LSM correctly identified twice more F3F4 patients than FIB-4&LSM.In the subgroup with LB≥20mm, LB confirmed F3F4 among concordant LST&LSM and FIB-4&LSM in 43/50(86%) vs 30/33(90%), NNS 1.6 and 1.8, respectively; the combination LST&LSM correctly identified 43% more F3F4 patients than FIB-4&LSM.DFP rate was higher with FIB4-&LSM than with LST&LSM (11.3% vs. 7%).
Conclusion: The combination LST&LSM outperforms FIB-4&LSM for the identification of MASH advanced fibrosis F3F4.

Disclosure
N. Alkhouri: Advisory Panel; Madrigal Pharmaceuticals, Inc., 89bio, Inc. Speaker's Bureau; Alexion Pharmaceuticals, Inc., Gilead Sciences, Inc. Advisory Panel; Altimmune Inc. Consultant; Boehringer-Ingelheim. Advisory Panel; Novo Nordisk. R. Nadeem: None. P. Leff: None. P. Mantry: None. R. Quiambao: Other Relationship; Fibronostics US Inc. J. Dupuy: None. M. Munteanu: Employee; Fibronostics. V. De Ledinghen: Advisory Panel; Fibronostics.
1337-P: Improvement of Visual Acuity May Protect against Cognitive Decline and Cortical Atrophy in Long-Duration Type 1 Diabetes
Authors: SHAH HETAL , DESALVO MATTHEW , HAIDAR ANASTASIA , VISHVA TEJA JANGOLLA SURYA , GREGORY YU MARC , ROQUE REBECCA , ZIEMNIAK NOLAN A. , VIEBRANZ ELIZABETH R. , WU I-HSIEN , FICKWEILER WARD , CHOKSHI TANVI , BILLAH TASHRIF , NING LIPENG , ADAM ATIF , SUN JENNIFER K. , RATHI YOGESH , FEANY MEL B. , KING GEORGE L. 
Published: June 2024

Cognitive decline is a grave complication in aging people with type 1 diabetes (T1D). We previously reported that compared to individuals without diabetes, those with long-duration T1D, the Joslin “Medalists” cohort, have impaired cognitive function, similar to people with type 2 diabetes (T2D). We further reported that Medalists (n=52), with T1D≥50 years, mean age of 71 years, mean HbA1c of 6.8 %, a good lipid profile and controlled hypertension, had significantly lower brain volumes compared to age-matched controls (n=20) on brain MRI (Alzheimer Disease Neuroimaging Initiative-3 protocol), but no significant differences in neurovascular lesions. In this study, we demonstrate that among Medalists, reduced total and regional brain volumes were cross-sectionally associated (p&lt;0.05) with female sex, worse psychomotor function and visual acuity, longer diabetes duration and higher inflammatory cytokines, but not with glycemic control (HbA1c, continuous glucose monitoring indices, advanced glycation end-products) or history of hypoglycemia. Interestingly, worse cognitive function and lower brain volumes, in addition to diabetic retinopathy, correlated selectively with thinning of the outer retinal nuclear layer (ONL, composed of photoreceptors) as assessed by Optical Coherence Tomography. Mediation analysis showed that psychomotor function mediated 58 % of the association between visual acuity and brain volume, and 44 % of the association between ONL thickness and brain volume. In a longitudinal subset (n=48), where Medalists had 2 or more cognitive visits with an average follow-up time of 4.8 years between visits, worse baseline visual acuity was associated with decline in psychomotor function over 3-5 years of follow-up. These findings suggest that future studies should investigate a potentially novel therapeutic approach of targeting improvement in visual acuity to prevent cognitive decline in people with long-duration T1D.

Disclosure
H. Shah: None. M. Desalvo: None. A. Haidar: None. S. Jangolla: None. M. Yu: None. R. Roque: None. N.A. Ziemniak: None. E.R. Viebranz: None. I. Wu: None. W. Fickweiler: None. T. Chokshi: None. T. Billah: None. L. Ning: None. A. Adam: None. J.K. Sun: Research Support; Optovue, Boehringer-Ingelheim, Novo Nordisk, Roche Pharmaceuticals. Other Relationship; Roche Pharmaceuticals. Research Support; Physical Sciences, Inc, Boston Micromachines. Y. Rathi: None. M.B. Feany: None. G.L. King: None.

Funding
NIH/NIDDK (3P30DK036836-34S1); The Beatson Foundation
1358-P: Diabetes Control and Complications in East India—Three-Year Results of LANDMARC Trial
Authors: RAIS NADEEM , CHOWDHURY SUBHANKAR , KALRA SANJAY , MUKHARJEE SAGARIKA , CHAUDHURY TIRTHANKAR , DAS SAMBIT , SAHA DEBASIS , SHAH SANJAY K. , BHATTACHARYYA SUPRATIK , MENON SHALINI K. , GADEKAR ARVIND , CHODANKAR DEEPA 
Published: June 2024

Glycemic control, therapy trends, and diabetes complications, were evaluated in LANDMARC, a prospective observational 3-year study (CTRI/2017/05/008452), of participants with T2D on ≥2 antihyperglycemic medications. This subgroup analysis of 52 sites from East India (Kolkata, Northeast) included 843 participants (mean [SD] baseline [BL] age: 53.1 [9.5] years, T2D duration: 8.7 [6.0] years and A1C: 7.9% [1.7]). At BL, most were insulin-naïve (n=647; 76.7%), mainly on OADs (n=644; 76.4%). At 3 years, 422 (57.5%) participants were on OADs only, while 295 (40.2%) were on insulin + OADs. Biguanides was the most prescribed drug class (BL: 793 [94.1%] and 3-years: 681 [92.8%]). Basal insulin usage increased from BL (n=99; 11.7%) to 3 years (n=213; 29.0%). The mean (SD) A1C, FPG, and PPG decreased by 1.0 (1.6) %, 23.4 (52.9) mg/dL, and 39.2 (82.6) mg/dL, respectively. East India had the highest proportion (53.1%; n=303) of participants with A1C&lt;7% at 3 years. CV death and neuropathy were most common macro and microvascular complications, respectively (Table). Hypertension and dyslipidemia were the most common CV risk factors. An understanding of regional trends in glycemia, therapy, and complications may help strategize future diabetes management practices in India.

Disclosure
N. Rais: None. S. Chowdhury: None. S. Kalra: Speaker's Bureau; Abbott, Bayer Inc., Novo Nordisk, Sanofi. S. Mukharjee: None. T. Chaudhury: None. S. Das: None. D. Saha: None. S.K. Shah: None. S. Bhattacharyya: None. S.K. Menon: Employee; Sanofi. A. Gadekar: None. D. Chodankar: Employee; Sanofi.

Funding
Sanofi