bluuebunny/crossref_metadata_2025_split huggingface dataset:
1264-P: Trends in Incidence and Prevalence of CFRD from the U.S. Cystic Fibrosis Foundation Patient Registry (CFFPR)
Authors: KOHLER CELIA L. , VIGERS TIMOTHY B. , SAKAMOTO CASEY , MORAN ANTOINETTE , SAGEL SCOTT , CHAN CHRISTINE L. 
Published: June 2023

Intro: The US CFFPR includes data from ~30,000 individuals with cystic fibrosis (CF). We aimed to examine trends in incidence and prevalence alongside screening rates of cystic fibrosis related diabetes (CFRD) from 2003-2018.
Methods: Prevalence of CFRD was defined as the percent of patients with CFRD in the registry in a given year. Annual incidence of CFRD was calculated as the number of new diagnoses per 1,000 person-years. Annual screening rates were defined as the percent of individuals without CFRD who underwent an oral glucose tolerance test (OGTT). Individuals &lt;10 years were excluded, and data were grouped by age decade.
Results: (Table 1) CFRD screening rates increased from 2003-2018, particularly in 10-18 yr olds. Although screening rates increased in adults as well, overall rates remain low. In 10-18 yr olds, the incidence of CFRD did not increase, and incident cases in adults decreased. The prevalence of CFRD is gradually increasing in adults.
Conclusions: Despite increased screening, the incidence of CFRD is stable or declining. CFRD prevalence is steadily rising in older adults. Screening rates and trends in CFRD incidence support a need for revised and potentially targeted screening algorithms. With increasing longevity in people with CF and rising prevalence of CFRD, there is great need for more specialists trained to care for a growing population of adults with CFRD.

Disclosure
C.L.Kohler: None. T.B.Vigers: None. C.Sakamoto: None. A.Moran: Advisory Panel; Abata Therapeutics, Dompé, Other Relationship; Novo Nordisk, Research Support; Abbott Diabetes. S.Sagel: None. C.L.Chan: None.
1268-P: One-Hour Glucose Thresholds for T2D Prediction—A Longitudinal Validation Study in the Korean Genome and Epidemiology Study (KoGES)
Authors: IM MYUNGSOO , RYANG SOREE , KIM DOOHWA , YI WOOK , KIM JINMI , KANG HYUK , JIN KIM YOUNG , JIN KIM YEONG , KIM MINSOO , JOO KIM IN , CHUNG STEPHANIE T. , SHERMAN ARTHUR , HA JOON , SOO KIM SANG 
Published: June 2023

One-hour glucose to predict prediabetes (PreDM=155mg/dL) and type 2 diabetes (T2D= 209 mg/dl) could improve detection with a shorter 1-hour OGTT, but thresholds need validation in longitudinal cohorts. We determined the risk of incident diabetes using the proposed thresholds with a 16-year longitudinal Korean data set (N=6381, BMI=24±4.0 kg/m2; Age 51.2±8.6 y). OGTT was conducted every 2 y, and 3 groups were assigned at baseline: low one-hour plasma glucose (1h-PG) with normal glucose tolerance (NGT) (low 1h-PG: FPG&lt;100, 2h-PG&lt;140, 1h-PG&lt;155), high 1h-PG with NGT (high 1h-PG: FPG&lt;100, 2h-PG&lt;140, 1h-PG≥155), and PreDM (100≤FPG&lt;126 or 140≤2h-PG&lt;200). Time to T2D was compared by the group in a subset (N=230). T2D risk was higher in high 1h-PG and PreDM than low 1h-PG group (HR:4.9 [3.5, 6.8] and 8.8 [6.5, 11.8]). With the subset of progressors, a linear mixed effect model showed that times to T2D from low and high 1h-PG were 24.8±18.5 y and 19.5±12.4 y (P&lt;0.05). The 1h-PG threshold for T2D, 209 mg/dl, was reached 7.5±14.5 y earlier than the 2h-PG threshold, 200 mg/dl. Conclusion: NGT with high 1h-PG is an intermediate state between low 1h-PG NGT and PreDM, and the 1h-PG threshold for T2D allows earlier diagnosis than the current 2h-PG threshold.

Disclosure
M.Im: None. I.Kim: None. S.T.Chung: None. A.Sherman: None. J.Ha: None. S.Kim: None. S.Ryang: None. D.Kim: None. W.Yi: None. J.Kim: None. H.Kang: None. Y.Kim: None. Y.Kim: None. M.Kim: None.

Funding
Busan Economic Promotion Agency; National Research Foundation of Korea (2022H1D3A2A01063552); Korea Health Industry Development Institute (HI18C2383); dkNET New Investigator Pilot Program in Bioinformatics; National Institute of Diabetes and Digestive and Kidney Diseases
1269-P: Trajectories of MicroRNAs Are Associated with Trajectories of Weight but Not Fasting Blood Glucose in the Diabetes Prevention Program Trial
Authors: LEWIS KIMBERLY A. , FLOREZ JOSE C. , AOUIZERAT BRADLEY , KANAYA ALKA M. , FLOWERS ELENA 
Published: June 2023

Introduction: Circulating microRNAs (miRs) are emerging as promising biomarkers of risk for type 2 diabetes (T2D) and responses to risk-reduction interventions, but no prior study has examined longitudinal associations between miRs and fasting blood glucose (FBG) or weight in adults at high risk for T2D. Prior studies have been cross-sectional or only measured longitudinal outcomes. We determined the association between longitudinal trajectories of miRs, FBG, and weight in a subset of participants from the Diabetes Prevention Program (DPP).
Methods: This study was a secondary analysis of a subset of participants (N=150) from DPP. A custom Fireplex assay was used to quantify 58 miRs from banked plasma samples collected at baseline, 1-year, and 2-years. Generalized linear mixed models were used to analyze associations between the miRs, FBG (mg/dL), and weight (kg) over time, adjusting for covariates (i.e., age, body mass index, race or ethnicity, gender, trial arm, and high density lipoprotein cholesterol). The Benjamini-Hochberg false discovery rate method was used to adjust for multiple comparisons.
Results: The sample was 73% female, 19% Black, 14% Hispanic, and mean age was 51 ± 11 years. At baseline, the participants were normotensive, obese, and had dyslipidemia and prediabetes. After adjusting for multiple comparisons, 39 miRs were significantly associated with weight, of which 12 miRs (let-7c-5p, miR-1066-5p, miR-1287-5p, miR-133b, miR-15a-5p, miR-15b-5p, miR-181c-3p, miR-197-5p, miR-23b-5p, miR-296-5p, miR-877-5p, miR-98-5p) remained significant after adjusting for covariates. In the FBG model, none of the miRs were significant.
Conclusions: Twelve miRs were associated with longitudinal weight but no miRs were associated with FBG trajectories in a subset of participants from the DPP trial. These miRs may provide mechanistic, diagnostic, or therapeutic insights about T2D prevention.

Disclosure
K.A.Lewis: None. J.C.Florez: Consultant; AstraZeneca, Novo Nordisk, Other Relationship; AstraZeneca, Merck & Co., Inc. B.Aouizerat: None. A.M.Kanaya: None. E.Flowers: None.

Funding
National Institute of Diabetes and Digestive and Kidney Diseases (R01DK124228, X01DK115999)
127-OR: The Effect of Tirzepatide during Weight Loss on Metabolic Adaption, Fat Oxidation, and Food Intake in People with Obesity
Authors: RAVUSSIN ERIC , SANCHEZ-DELGADO GUILLERMO , MARTIN CORBY K. , NISHIYAMA HIROSHI , LI JING , URVA SHWETA , PRATT EDWARD J. , COSKUN TAMER , MILICEVIC ZVONKO , HAUPT AXEL 
Published: June 2023

We hypothesized that tirzepatide (TZP) causes body weight loss by reducing food intake and, according to pre-clinical studies, decreasing metabolic adaptation during weight loss. In a randomized blinded clinical trial in people with obesity (PWO) undergoing caloric restriction, we investigated the effect of TZP vs Placebo (PBO) on energy metabolism after targeting a 10% (±2%) weight loss. In this 18wk phase 1 study, 55 PWO, were randomized (1:1) to TZP 15 mg or PBO (mean baseline body weight 102.5 vs 103.1 kg, respectively). The primary objective was to investigate the change from baseline in sleeping metabolic rate (SMR) measured by whole-room indirect calorimetry. Secondary measures were changes in 24-hr sedentary energy expenditure, 24-hr respiratory quotient (RQ), sleeping RQ, substrate utilization, body composition (BC) and food intake. TZP caused greater weight loss than PBO (-16.7 kg vs -8.3 kg; p&lt;0.001). The decreases in SMR and 24-hr energy expenditure were not different between groups after adjusting for changes in body weight and BC. However, TZP significantly reduced 24-hr RQ and sleeping RQ compared to PBO; thus, significantly increasing fat oxidation while decreasing carbohydrate and protein oxidation rates. TZP also significantly reduced food intake. Overall, TZP did not affect metabolic adaptation but significantly increased fat oxidation.

Disclosure
E.Ravussin: Advisory Panel; Novartis, Research Support; Eli Lilly and Company, Novartis AG. A.Haupt: Employee; Lilly, Stock/Shareholder; Lilly. G.Sanchez-delgado: Research Support; Eli Lilly and Company, Novo Nordisk, Abbott, Maastricht instruments, Vyaire Medical, ParvoMedics, Cosmed, Speaker's Bureau; Eli Lilly and Company. C.K.Martin: Board Member; EHE Health, Wondr Health, Other Relationship; ABGIL, Research Support; Pack Health, Evidation Health, Lilly, WW International, Inc. H.Nishiyama: None. J.Li: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. S.Urva: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. E.J.Pratt: Employee; Eli Lilly and Company. T.Coskun: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. Z.Milicevic: Employee; Eli Lilly and Company.

Funding
Eli Lilly and Company
1272-P: Fatty Liver Change and Type 2 Diabetes in Older Adults with Prediabetes
Authors: HWAN PARK JUNG , CHUL WON JONG 
Published: June 2023

Background: No data are available on the association between changes in fatty liver disease (FLD) and the risk of type 2 diabetes in elderly individuals with prediabetes.
Methods: A total of 156,984 elderly subjects with prediabetes who underwent national health screening in 2009 and 2011 were followed up. FLD was defined as a change in the fatty liver index (FLI) with a cut-off of 60 for steatosis and 30 as a reference. Multivariable Cox proportional hazards regression was used to calculate the hazard ratio (HR) and confidence intervals (CIs) for type 2 diabetes according to the changed status of the FLD.
Results: During a median of 8.35 years of follow-up, 29,422 (18.7%) elderly individuals with prediabetes developed type 2 diabetes. Multivariable adjusted HRs of type 2 diabetes according to FLI change were 2.22 (95% CI 2.11-2.34) in individuals with both FLI ≥ 60 compared to those with both FLI &lt; 30. Although weight loss ≥5% was associated with a 7% lower risk of type 2 diabetes (95% CI 0.90-0.96), those with resolved FLD, new FLD, and persistent FLD along with weight loss at the same time period had an increased risk of type 2 diabetes by 30%, 36%, and 53%, respectively. The risk of type 2 diabetes did not increase in individuals with FLD-free status, regardless of weight change.
Conclusions: The presence and change of FLD is an important factor for the development of type 2 diabetes in elderly individuals with prediabetes.

Disclosure
J.Park: None. J.Won: None.
1274-P: High Prevalence of Type 2 Diabetes without Traditional Risk Factors among Men Who Inject Drugs in Chennai, India
Authors: CHEBROLU PUJA , MCFALL ALLISON , BROWN TODD , SRIKRISHNAN AK , SURESH KUMAR M. , SYED IQBAL H. , MATHAD JYOTI S. , LUCAS GREGORY , SOLOMON SUNIL S. , MEHTA SHRUTI 
Published: June 2023

South Asians with type 2 diabetes (T2DM) are predominately normal- or underweight, due to early loss of beta cell function in addition to insulin resistance. Substances of misuse are an additional stressor on beta cells and may lead to an increased prevalence of T2DM. We determined the prevalence of T2DM and its correlates among people who inject drugs (PWID) in Chennai. We conducted a cross-sectional analysis among male PWID. T2DM was defined as fasting glucose ≥126mg/dL or a prior diagnosis. Logistic regression was used to identify correlates of T2DM. Among 1039 PWID, median age was 38.9 years (IQR 32.1, 44.9) and median BMI was 19.7 kg/m2 (IQR 17.8, 22.9). The most common injected substance was heroin (94.8%, n=998). T2DM prevalence was 8.4% (n=87), among whom 60% were underweight or normal weight and 64% had normal waist circumference. On univariable regression analysis, T2DM was not associated with greater frequency of drug use, type of drug, HIV, or Hepatitis C. On multivariable analysis, age &gt;40 years (OR 3.0, 95% CI 1.3-6.8) and high waist circumference (OR 1.9, 95% CI 1.1-3.2) were associated with T2DM. The prevalence of T2DM among PWID is similar to India as a whole, though most cases would not be detected by screening using weight or waist circumference. Integrating universal metabolic disease screening into substance abuse treatment programs may help reduce morbidity and mortality from T2DM.

Disclosure
P.Chebrolu: None. S.Mehta: Research Support; Abbott Diagnostics. A.Mcfall: None. T.Brown: Advisory Panel; Merck & Co., Inc., Gilead Sciences, Inc., ViiV Healthcare, Jannsen. A.Srikrishnan: None. M.Kumar: None. H.Iqbal: None. J.S.Mathad: None. G.Lucas: None. S.S.Solomon: Research Support; Gilead Sciences, Inc., Abbott.

Funding
National Institute on Drug Abuse (R01DA026727); Fogarty International Center (D43TW009337); Weill Cornell Department of Medicine; National Institute of Allergy and Infectious Diseases (K24AI120834 to T.B.)
1276-P: Level of Injury Affects Body Mass Changes but Has No Influence on Alteration in Glycaemic Index in Persons with Spinal Cord Injury—A Prospective Cohort Study
Authors: HOEKSTRA SVEN , MADER MICHAEL J. , TRBOVICH MICHELLE , SALEHI MARZIEH 
Published: June 2023

In able-bodied individuals, an increase in body mass index (BMI) enhances the risk of glucose tolerance deterioration and diabetes mellitus (DM). This relationship can be altered after spinal cord injury (SCI) due to muscle atrophy and the potential effects of autonomic dysfunction on glucose metabolism, which are more exaggerated in those with tetraplegia (TP) compared to paraplegia (PP). Here, we investigated the relationship between longitudinal changes in BMI and glycated haemoglobin (HbA1c), as well as the effect of level of injury (LOI) on this relationship, among nondiabetic patients with SCI. In a retrospective cohort of veterans with SCI, electronic data of BMI, LOI, glucose concentration and HbA1c were obtained from Jan 2001 to Dec 2021. A total of 728 patients without DM at baseline were identified (LOI: 350 TP/378 PP, age: 52±15 years, sex: 690 male/38 female), and followed up for a mean of 94 months (TP) and 114 months (PP). At baseline, using the SCI-specific BMI classification (normal weight BMI&lt;22 kg/m2, obesity BMI&gt; 25 kg/m2), 48% of TP and 65% of PP were obese at baseline (BMI TP: 25.6±5.9 kg/m2; PP: 28.0±6.6 kg/m2,; p&lt;0.001). HbA1c at baseline was similar between TP and PP (5.21±0.59% vs 5.27±0.70%, p = 0.175). At the end of follow-up, BMI was increased to a larger extent in PP compared with TP (-0.02±4.11 kg/m2 in TP, +0.55±4.31 kg/m2 in PP; p = 0.05). However, HbA1c changes did not differ between TP versus PP (p = 0.323). The final BMI was associated with baseline BMI (p&lt;0.001) and LOI (p = 0.007), but the last HbA1c value was only associated with baseline HbA1c (p&lt;0.001) and not LOI (p = 0.821) or baseline BMI (p = 0.605). Together, these findings indicate that neither LOI nor BMI appear useful predictors of DM development in persons with SCI.

Disclosure
S. Hoekstra: None. M.J. Mader: None. M. Trbovich: None. M. Salehi: None.
1285-P: Disparities in Screening, Diagnosis, and Treatment of Type 2 Diabetes Mellitus among Persons with HIV
Authors: MILLMAN NINA , KOETHE JOHN , TURNER MEGAN , BOURGI KASSEM , STERLING TIMOTHY , REBEIRO PETER 
Published: June 2023

Background: Persons with HIV (PWH) have normal life expectancy on antiretroviral therapy (ART), but a rising burden of type 2 diabetes mellitus (DM) disproportionately affecting women and Black individuals threatens these gains. We therefore assessed whether disparities in DM incidence are reflected in disease screening and treatment by HIV providers.
Methods: PWH on ART with ≥2 clinic visits at a large HIV clinic in the southeast from 2007-2022 contributed data, if they did not have prevalent DM at enrollment: existing ICD-9/10 diagnoses of DM, antidiabetic medication, hemoglobin A1c (A1c) ≥6.5%, or random glucose ≥200 mg/dL. We assessed the association of demographic and clinical factors with DM screening by A1c and, among those with incident DM by A1c or ICD-9/10 diagnosis or random glucose, DM treatment and achievement of goal (i.e., A1c ≤7.0%). For each “care continuum” step, we used modified Poisson regression to estimate adjusted incidence rate ratios (IRRs) of reaching the next step by these factors.
Results: Among 5450 PWH enrolling, 3762 (69%) were screened for DM by A1c, of whom 1001 (27%) had dysglycemia (A1c &gt;5.7%) and 162 (4.3%) were diagnosed with DM. Among 186 PWH with incident DM, 136 (73%) initiated antidiabetic medication and 94 (84%) achieved an A1c ≤7.0%. Older, Black, and overweight or obese PWH were more likely to be screened (IRRs=1.0-1.1, p&lt;0.05 each) and diagnosed with DM (IRRs=1.8-2.8, p&lt;0.05 each), but there was no significant sex difference in the adjusted model. Of those screened and diagnosed with DM, no clinical or demographic factors were associated with DM treatment initiation or achieving A1c goal.
Conclusions: Older, Black, and overweight/obese PWH were more likely to be screened for and diagnosed with DM. However, we found no disparities in initiating DM medications or achieving an A1c goal. While DM screening among PWH warrants improvement, women and Black PWH, two groups with a higher risk of DM, did not appear to be treated disparately in this population.

Disclosure
N.Millman: None. J.Koethe: Advisory Panel; Merck Sharp & Dohme Corp., Janssen Pharmaceuticals, Inc., Gilead Sciences, Inc., Theratechnologies, Research Support; Merck Sharp & Dohme Corp. M.Turner: None. K.Bourgi: Advisory Panel; Gilead Sciences, Inc., Theratechnologies, Research Support; Gilead Sciences, Inc. T.Sterling: None. P.Rebeiro: Consultant; Gilead Sciences, Inc., Janssen Pharmaceuticals, Inc., Research Support; National Institutes of Health.

Funding
National Institutes of Health (P30AI110527)
1288-P: Predictive Modeling of Type 2 Diabetes and Complications Using Machine Learning
Authors: KHATTAB AHMED , CHEN SHANG-FU , SADAEI HOSSEIN J. , TORKAMANI ALI 
Published: June 2023

Background: Given the prevalence of T2D worldwide, early detection and prevention of both T2D and its complications are of utmost importance. We propose comprehensive person-centered machine learning (ML) risk prediction models for T2D and complications.
Methods: From the UK biobank (UKBB), we identified a prevalent T2D group at baseline (9,136 cases and 108,615 controls), an incident T2D group over 10 years of follow-up (29,845 cases and 354,818 controls), and an incident diabetic kidney disease (DKD) group (4435 cases and 33083 controls). We included 3 types of risk factors: clinical (biomarkers and diseases with known diabetes risk like CVD), genetic in the form of polygenic risk scores (PRS) for the pathogenesis of T2D and other clinical risks, and lifestyle. For each group, we trained a model using decision-tree-based ensemble ML algorithm. We then used SHAP values to determine feature importance and chose the top 10 predictors to build a final model.
Results: The T2D prevalence model included 74 features and had an AUC of 0.97, and area under the precision-recall curve (PR AUC) of 0.83. The final model with the top features (HbA1c, T2D PRS, primary HTN, LDL, major dietary changes in the last 5 years, age, plasma glucose, waist circumference, and total cholesterol) had an AUC of 0.98, and PR AUC of 0.88. The T2D incidence model included the same 74 features and had an AUC of 0.92, and PR AUC of 0.57. The final model with the top features had an AUC of 0.93, and PR AUC of 0.65. The DKD risk prediction model comprised 85 features, and it had an AUC of 0.81, and PR AUC of 0.37. The final model with the top features (Cystatin C, primary HTN, eGFR, age, HbA1c, T2D PRS, urea, BMI, BMI PRS, NAFLD PRS) had an AUC of 0.80, and PR AUC of 0.39.
Conclusions: This study is an ongoing work; our interim results show the capability of ML to identify relevant risk factors and that comprehensive ML models could help clinicians decide on more precise and individualized T2D screening and treatment approaches to prevent T2D and complications.

Disclosure
A.Khattab: None. S.Chen: None. H.J.Sadaei: None. A.Torkamani: None.
129-LB: A Comparison of CGM Metrics in People with Type 1 Diabetes Treated with Different Insulin Modalities
Authors: NØRGAARD KIRSTEN , RANJAN AJENTHEN G. , LAUGESEN CHRISTIAN , TIDEMAND KATRINE G. , ANDERSEN HENRIK U. , SVENSSON JANNET , SELMER CHRISTIAN , GREEN ALLAN , VISTISEN DORTE , CARSTENSEN BENDIX 
Published: June 2023

Background: There is a lack of real-world data comparing CGM data across different insulin treatment modalities. This study examined the association between CGM-measured glycemic metrics and different insulin treatment modalities in a large type 1 diabetes clinic.
Methods: Individuals with type 1 diabetes attending Steno Diabetes Center Copenhagen, Denmark, were eligible for inclusion if they had ≥70% CGM data available within a four-week period within the past year. Participants were divided into four groups based on their insulin treatment: multiple daily injections (MDI +/- alarms), sensor unintegrated insulin pumps (SUP +/- alarms), sensor-augmented insulin pumps (SAP), and automated insulin delivery systems (AID). The latest measured HbA1c and CGM were analyzed.
Results: Of 6,319 attendees, 2,689 met the inclusion criteria: 1405 with MDI, 453 with SUP, 307 with SAP, and 524 with AID. Compared to the MDI - alarms group, a clinically relevant lower mean HbA1c was found in the SAP (0.5%; 95% CI:0.3-0.6) and the AID group (0.7%; 95% CI:0.6-0.9) (adjusted for sex, age and diabetes duration). CGM metrics for the four different treatment groups are presented.
Conclusion: The results suggest that AID was superior to all other insulin delivering systems with CGM. Although results may be affected by bias due to indication, the expansion of AID use to a wider type 1 population should be considered.

Disclosure
K. Nørgaard: Advisory Panel; Medtronic, Novo Nordisk, Research Support; Medtronic, Dexcom, Inc., Novo Nordisk, Zealand Pharma A/S, Speaker's Bureau; Medtronic, Novo Nordisk, Stock/Shareholder; Novo Nordisk. B. Carstensen: Stock/Shareholder; Novo Nordisk. A. G. Ranjan: None. C. Laugesen: Employee; Novo Nordisk A/S, Stock/Shareholder; Novo Nordisk A/S. K. G. Tidemand: None. H. U. Andersen: None. J. Svensson: Other Relationship; Medtronic, Sanofi, Rubin MEdical, Research Support; Novo Nordisk, Medtronic, Stock/Shareholder; Novo Nordisk. C. Selmer: Other Relationship; Boehringer-Ingelheim, Novo Nordisk. A. Green: None. D. Vistisen: Research Support; Bayer Inc., Sanofi, Boehringer-Ingelheim, Stock/Shareholder; Novo Nordisk A/S.