bluuebunny/crossref_metadata_2025_split huggingface dataset:
753-P: RGT-075, an Orally Efficacious Small Molecule GLP-1R Full Agonist in Cynomolgus Monkey Models
Authors: LIU FENG , SUN XIANQIANG , HUANG WEI , GUO WEI , LIN JING , ZHONG WENGE 
Published: May 2022

Activation of Glucagon-like peptide-1 receptor (GLP-1R) by peptides has been proven clinically to be a very effective treatment approach for type 2 diabetes (T2D) and obesity. Here we describe a small molecule GLP-1R full agonist RGT-075 identified through Computer Accelerated Rational Design (CARD) (Ma et al., Cell Research 2020) with nM potency in G-protein coupled cAMP signaling. RGT-075 displayed much reduced activity in receptor-mediated β-arrestin recruitment and subsequent internalization. It was shown to be selective against other class B G protein-coupled receptors (GPCRs) and was only active for human and monkey GLP-1R. It demonstrated favorable oral bioavailability and pharmacodynamic profile for once-daily oral dosing in monkeys. In food-induced glucose intolerant and prediabetic cynomolgus monkeys, oral administration of RGT-075 improved glucose tolerance to a level comparable to injectable liraglutide. In food-induced type 2 diabetic monkeys, once-daily oral administration of RGT-075 reduced food intake and fasting plasma glucose levels. Together, these data support the development and advancement of RGT-075 into the clinic as a potential therapeutic for T2D and obesity.

Disclosure
F.Liu: Employee; Regor Pharmaceuticals Inc. X.Sun: Employee; AutoDrug Biotech Co. Ltd., Qilu Regor Therapeutics Inc. W.Huang: None. W.Guo: None. J.Lin: None. W.Zhong: None.
761-P: Long-Term Glycemic Control in Adult Participants Using Control-IQ Technology: Real-World Evidence
Authors: GRAHAM RISHI , SINGH HARSIMRAN , ALENCAR GABRIEL , MUELLER LARS , MANNING MICHELLE L. , WHITE KIRSTIN N. , WHEATCROFT ALEX , CARMELO KAREN , ARONOFFSPENCER ELIAH S. , HABIF STEPHANIE , PINSKER JORDAN E. 
Published: May 2022

The Control-IQ Observational (CLIO) Study is an ongoing prospective, real-world evaluation of the Tandem Diabetes Care t:slim X2 insulin pump with Control-IQ technology in people with T1D.
We assessed baseline HbA1c and Glucose Management Indicator (GMI) derived from sensor data from adult participants who completed the 12-month CLIO study. Adults ages ≥18 years who had ≥75% overall CGM use during this time were included in the analysis. Outcomes were analyzed using a Wilcoxon test.
In all, 1,1participants (882 prior pump and 225 prior MDI users) , median age 41 years (IQR 30-54) , 57.2% female met the inclusion criteria. Median GMI was significantly lower at 12 months (7.0% [6.7-7.4]) vs. baseline HbA1c (7.2% [6.7-8.1]) (p&lt;0.0001) . Participants ages 18-30 and 46-64 showed the greatest improvement from baseline (0.3% reduction) (p&lt;0.0001) , while older adults (≥65-years) showed the lowest GMI at study end (6.9% [6.6-7.2]) . Prior MDI users reported higher HbA1c at baseline (7.7% [IQR=6.8-9.0]) vs. prior pumpers (7.2% [6.7-8.0]) . However, GMI at 12 months reflected glycemic improvements across all prior therapy modalities (prior MDI=7.0% [6.7-7.3]; prior pump=7.1% [6.7-7.4]) (p&lt;0.0001) .
This real-world evidence demonstrates significant glycemic improvements with Control-IQ technology in adults, regardless of prior therapy modality, that are sustained long-term across diverse groups of users.

Disclosure
R.Graham: Research Support; Tandem Diabetes Care, Inc. S.Habif: Employee; Tandem Diabetes Care, Inc. J.E.Pinsker: Employee; Tandem Diabetes Care, Inc., Stock/Shareholder; Tandem Diabetes Care, Inc. H.Singh: Employee; Tandem Diabetes Care, Inc. G.Alencar: Employee; Tandem Diabetes Care, Inc. L.Mueller: Employee; Tandem Diabetes Care, Inc., Research Support; Dexcom, Inc., Stock/Shareholder; Tandem Diabetes Care, Inc. M.L.Manning: Employee; Tandem Diabetes Care, Inc. K.N.White: Employee; Tandem Diabetes Care, Inc. A.Wheatcroft: Employee; Tandem Diabetes Care, Inc. K.Carmelo: None. E.S.Aronoffspencer: Research Support; Tandem Diabetes Care, Inc.

Funding
Tandem Diabetes Care
77-LB: How the Incidence of Anxiety and Other Psychosocial Factors Impact Quality of Life in Parents of Children with Type 1 Diabetes Who Used a Continuous Glucose Monitor (CGM) —A Systematic Review
Authors: SAWYER BAILEE C. , HUTSON LEANNE M. , CIPRICH AMANDA P. 
Published: May 2022

This study aimed to perform a systematic review related to how anxiety and other psychosocial factors impact quality of life in parents and their children with type 1 diabetes (T1D) who utilize a continuous glucose monitor (CGM) . Electronic databases and published references were utilized to establish studies published between 2012 and 2022. There were 29 studies identified that included the search terms T1D, anxiety, parents, CGM. After careful quantitative measurements analysis, seven studies remained. The researchers of the seven studies utilized CGM in children with T1D and surveyed parental anxiety, psychosocial care and quality of life. The results presented in Table 1 and Table 2 show implications for programming and treatment to support parents of T1D children in reducing the psychosocial and quality of life outcomes when utilizing CGM technology.

Disclosure
B. C. Sawyer: None. L. M. Hutson: None. A. P. Ciprich: Other Relationship; DarioHealth Corp., Medtronic.
771-P: Acceptance of Decision Support Recommendations Improves Time in Range for People Living with Type 1 Diabetes on Multiple Daily Injections
Authors: CASTLE JESSICA R. , ESPINOZA ALEJANDRO Z. , TYLER NICHOLE S. , WILSON LEAH M. , MOSQUERA-LOPEZ CLARA M. , PINSONAULT JOSEPH , DODIER ROBERT , OGANESSIAN SOS M. , BRANIGAN DEBORAH , GABO VIRGINIA , EOM JAE H. , EL YOUSSEF JOSEPH , RAMSEY KATRINA , KUSHNER TAISA , WINTERS-STONE KERRI , CAFAZZO JOSEPH A. , JACOBS PETER G. 
Published: May 2022

DailyDose is a decision support system developed at Oregon Health & Science University designed for people with T1D on MDI to improve glycemic control. It connects with Dexcom G6 and Medtronic's InPen. DailyDose runs on a smartphone and calculates insulin doses using CGM value and trend, IOB, carbohydrate amount, and exercise information. The system analyzes CGM and insulin data and automatically provides weekly recommendations on insulin settings, such as basal insulin dose and carbohydrate ratios, based on a k-nearest neighbors algorithm. Twenty-four adults with T1D used DailyDose for 8 weeks. The primary outcome was change in % time in range (TIR, 70-180 mg/dL) on CGM comparing the two week run-in period before starting DailyDose vs. final two weeks of DailyDose use. A mixed effects model was used to determine the impact of the % of accepted recommendations on change in % TIR. Users who accepted and followed recommendations showed a mean week-to-week improvement in TIR of 2.0% (Figure) . The mixed effects model shows week-by-week TIR increased by 7.8% when recommendations were accepted compared with not accepted (CI, 3-12%, P=.001) . Overall, there were no significant differences between TIR or time in hypoglycemia comparing the run-in period and the final two weeks of use. Further work is needed to encourage people using decision support systems to follow recommendations.

Disclosure
J.R.Castle: Advisory Panel; Insulet Corporation, Novo Nordisk, Zealand Pharma A/S, Stock/Shareholder; Pacific Diabetes Technologies. V.Gabo: None. J.H.Eom: None. J.El youssef: None. K.Ramsey: None. T.Kushner: Consultant; Tandem Diabetes Care, Inc. K.Winters-stone: None. J.A.Cafazzo: None. P.G.Jacobs: Other Relationship; Pacific Diabetes Technologies, Research Support; Dexcom, Inc. A.Z.Espinoza: None. N.S.Tyler: None. L.M.Wilson: n/a. C.M.Mosquera-lopez: None. J.Pinsonault: None. R.Dodier: None. S.M.Oganessian: None. D.Branigan: None.

Funding
The Leona M. and Harry B. Helmsley Charitable Trust (Grant 2018PG-T1D001) .
782-P: Swimming with Hybrid Closed-Loop: Connectivity Data from Seven Swimmers in the Omnipod 5 Trial
Authors: HUGHES MICHAEL S. , KINGMAN RYAN S. , HSU LIANA , LAL RAYHAN , BUCKINGHAM BRUCE A. , ZAHARIEVA DESSI P. 
Published: May 2022

The Omnipod® 5 Automated Insulin Delivery System is a waterproof (IP28) hybrid closed-loop system where the continuous glucose monitor (CGM) communicates directly with the algorithm on the Pod to modulate insulin delivery automatically. However, the system relies on Bluetooth® wireless technology, a radiofrequency with limited underwater transmission ability.
Seven frequent swimmers participating in the Omnipod 5 trial reported 63 separate swimming sessions ranging 25-143 min (mean 75±30 min) . CGM-to-Pod wireless communication during swims had a median (IQR) success rate of 17% (5%, 29%) ; however, the communication success rate was 100% across all swims with CGM-to-Pod distance &lt;8 cm (device edge to edge, n=5) . Despite communication limitations, percent time in automated mode was 99% during swims and automated modulations to insulin delivery continued to occur as needed (Table) .
These results suggest that while in-water communication is challenged, Pod to CGM placement strategies can be practiced to better improve the connectivity. Moreover, the system by design remains in automated mode when CGM values are missed and does not revert the user back to manual mode settings.

Disclosure
M.S.Hughes: Research Support; Insulet Corporation. R.S.Kingman: Research Support; Insulet Corporation. L.Hsu: Research Support; Insulet Corporation, Stock/Shareholder; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc. R.Lal: Advisory Panel; Provention Bio, Inc., Consultant; Abbott Diabetes, Biolinq, Capillary Biomedical, Inc., Deep Valley Labs, Gluroo, Morgan Stanley, Tidepool. B.A.Buckingham: Advisory Panel; Arecor, Lilly Diabetes, Medtronic, Other Relationship; Insulet Corporation, Research Support; Insulet Corporation, Lilly Diabetes, Medtronic. D.P.Zaharieva: Research Support; Insulet Corporation, International Society for Pediatric and Adolescent Diabetes, Leona M. and Harry B. Helmsley Charitable Trust.

Funding
Gift funding from Insulet Corporation
81-OR: High Throughput Proteomics Identifies Neurofilament Light Chain (NFL) as a Novel Blood Biomarker for Detection of Diabetic Sensorimotor Polyneuropathy
Authors: MAALMI HAIFA , STROM ALEXANDER , HAUCK STEFANIE M. , ZAHARIA OANA P. , STRASSBURGER KLAUS , BÖNHOF GIDON J. , RATHMANN WOLFGANG , TRENKAMP SANDRA , BURKART VOLKER , SZENDROEDI JULIA M. , ZIEGLER DAN , RODEN MICHAEL , HERDER CHRISTIAN 
Published: May 2022

Background: The neurofilament light chain (NFL) is a polypeptide abundant in neuronal axons, reflecting ongoing neuroaxonal degeneration when detected in the blood. However, no previous study has explored NFL as a potential biomarker for diabetic sensorimotor polyneuropathy (DSPN) , a neurodegenerative disease of the peripheral nervous system commonly found in individuals with diabetes. We hypothesized that higher serum NFL levels are associated with DSPN.
Methods: We used data from the German Diabetes Study, a prospective observational study that includes adults with recent-onset diabetes (&lt;1 year) . DSPN was assessed using Toronto consensus criteria. Serum levels of NFL were measured using proximity extension assay, and Poisson regression was used to estimate associations between serum NFL and prevalent DSPN.
Results: From a total of 423 participants, 66 (16%) had DSPN. Serum NFL was inversely correlated with age, eGFR and nerve conduction velocities in motor and sensory nerves (all P Spearman &lt;0.0001) . After adjustment for age, sex, waist circumference, height, HbA1c, diabetes duration, diabetes type, cholesterol, eGFR, hypertension, cardiovascular diseases, use of lipid-lowering drugs, and NSAIDs, higher serum levels of NFL were associated with prevalent DSPN (RR (95% CI) 1.92 (1.50, 2.45) (P &lt; 0.0001) .
Conclusion: NFL might be useful for detecting DSPN in recent-onset diabetes, but validation in other studies will be required. Considering the absence of FDA-approved biomarkers for DSPN, NFL might be a promising biomarker that could improve the management of DSPN.

Disclosure
H. Maalmi: None. A. Strom: None. S.M. Hauck: None. G.J. Bönhof: None. W. Rathmann: Consultant; IQVIA Inc. Speaker's Bureau; Boehringer Ingelheim International GmbH, Novo Nordisk. S. Trenkamp: None. V. Burkart: None. J.M. Szendroedi: Consultant; Boehringer Ingelheim International GmbH. D. Ziegler: None. M. Roden: Advisory Panel; Eli Lilly and Company. Research Support; Boehringer Ingelheim International GmbH, Nutricia. Speaker's Bureau; Novo Nordisk. C. Herder: Research Support; Sanofi.

Funding
DZD (FKZ82DZD02D2G)
832-P: Five-Year Weight and Glycemic Outcomes following a Very-Low-Carbohydrate Intervention Including Nutritional Ketosis in Patients with Type 2 Diabetes
Authors: ATHINARAYANAN SHAMINIE J. , VANTIEGHEM MICHELLE , MCKENZIE AMY L. , HALLBERG SARAH , ROBERTS CAROLINE G.P. , VOLK BRITTANIE M. , ADAMS REBECCA N. , RATNER ROBERT E. , VOLEK JEFF , PHINNEY STEPHEN 
Published: May 2022

Objective: We previously reported long term effectiveness of a very low carbohydrate intervention including nutritional ketosis (VLCI) delivered via continuous remote care (CRC) for improving weight and glycemia at 2 years in people with type 2 diabetes (T2D) . We assessed 5-year changes to determine if the intervention is sustainable, durable, and effective over a longer period of time.
Research Design and Methods: Patients with T2D who were initially enrolled in a 2 year non-randomized, controlled clinical trial received a CRC emphasizing a VLCI. These patients were offered to continue for an additional 3 years of prospective follow-up. Of the 200 patients completing 2 years, 169 (84.5%) patients consented to the extension and 122 (72.2%) were retained at 5 years. Among those who extended, baseline versus 5 year differences in weight and glycemic outcomes were assessed using linear mixed effects models in an intent-to-treat analysis. P-values were adjusted using Holm-Bonferroni correction.
Results: At five years, there were persistent improvements in weight from 116.4 to 107.6 kg (-8.8 kg, 95%CI [-11.0, -6.6]) , fasting insulin from 25.8 to 24.5 mIU/L (-7.9 mIU/L, 95%CI [-10.0, -5.8]) , and HOMA-IR from 9.1 to 6.6 (-2.5, 95%CI [-3.5, -1.5]) (all adjusted p-values &lt;0.05) . Total diabetes medications were reduced 46.6%, and 59.9% excluding metformin were deprescribed. The percent of patients prescribed diabetes medications significantly decreased at 5 years (from 85.2% to 71.3%; p&lt;0.01) , including patients taking sulfonylureas (from 27.0% to 4.9%) , insulin (from 26.2% to 13.1%) , and SGLT2i (from 10.7% to 2.5%) . Despite less medication use, HbA1c improved from 7.5 to 7.2% (-0.3%, 95%CI [-0.6, 0.0], unadjusted p-value&lt;0.05) .
Conclusions: Over 5 years follow-up, the VLCI with CRC showed excellent retention, sustained clinically significant weight loss, and stable glycemic control with reduced dependency on antidiabetes medications.

Disclosure
S.J.Athinarayanan: Employee; Virta Health Corp. S.Phinney: Employee; Virta Health Corp. M.Vantieghem: Employee; Virta Health Corp., Stock/Shareholder; Virta Health Corp. A.L.Mckenzie: Employee; Virta Health Corp., Stock/Shareholder; Virta Health Corp. S.Hallberg: Advisory Panel; Atkins Nutritionals Inc., Employee; Virta Health Corp., Stock/Shareholder; Virta Health Corp. C.G.P.Roberts: Employee; Virta Health Corp., Stock/Shareholder; Virta Health Corp. B.M.Volk: Employee; Virta Health Corp., Stock/Shareholder; Virta Health Corp. R.N.Adams: Employee; Virta Health Corp. R.E.Ratner: Employee; Virta Health Corp. J.Volek: Advisory Panel; Abbott Diagnostics, Simply Good Foods, Stock/Shareholder; Virta Health Corp.
87-LB: Treatment Intensification after Glucagon-Like Peptide-1 Receptor Agonists Therapy in Type 2 Diabetes—The RESTORE-G Real-World Study
Authors: CANDIDO RICCARDO , NICOLUCCI ANTONIO , LAROSA MONICA , ROSSI MARIA CHIARA , NAPOLI RAFFAELE ,   RESTORE-G STUDY GROUP
Published: May 2022

Data on therapeutic approaches after GLP1-RA treatment failure are scant. Intensification strategies include addition of basal insulin to GLP1-RA, as free or fixed-ratio combination, as well as the switch to insulin therapy alone. Aim of the study was to assess treatment intensification approaches after GLP1-RA treatment and their effectiveness and safety in the real-word setting. A retrospective, multicenter study, based on electronic medical records collected between January 2011 and September 2021 was conducted. Overall, data relative to 17.861 patients with at least 1 prescription of GLP1-RA were collected by 32 centers in Italy. Among them, 3.164 (17.7%) needed intensification and were treated according to the three main treatment approaches reported in table. Intensification occurred after a median of 27 months. Data showed clinical inertia during and after GLP1-RA treatment, as documented by high HbA1c levels at intensification and suboptimal basal insulin dosing in all cohorts. Effectiveness and safety were documented in all cohorts, but many patients did not reach the HbA1c target. Low rate of hypoglycemia was documented especially in patients initiating FRC (Table) .
In conclusion, in the years to come, advancing therapy with timely addition and adequate titration of new basal insulin analogues or FRC may contribute to overcome clinical inertia.

Disclosure
R. Candido: None. A. Nicolucci: Board Member; AstraZeneca, Research Support; Novo Nordisk, PIKDARE S. p. A., Sanofi, Shionogi & Co., Ltd., Swedish Orphan Biovitrum AB, Speaker’s Bureau; Eli Lilly and Company. M. Larosa: Employee; Sanofi. M. Rossi: Research Support; AlfaSigma, Novo Nordisk, Sanofi, Shionogi & Co., Ltd., SOBI. R. Napoli: Advisory Panel; Eli Lilly and Company, Novo Nordisk, Sanofi, Speaker’s Bureau; AstraZeneca, Boehringer Ingelheim International GmbH, Merck Sharp & Dohme Corp., Novo Nordisk, Sanofi. Restore-g study group: n/a.

Funding
Sanofi
882-P: Deep Learning Model for Predicting Hospital Readmission among People with Diabetes
Authors: HAI AMEEN ABDEL , WEINER MARK , PARANJAPE ANURADHA , LIVSHITS ALICE , BROWN JEREMIAH , OBRADOVIC ZORAN , RUBIN DANIEL J. 
Published: May 2022

Current models for predicting 30-day readmission risk among people with diabetes vary in performance. We previously published the Diabetes Early Readmission Risk Indicator (DERRITM) , a logistic regression (LR) model with modest predictive performance (C-statistic 0.69) . The current study aims to develop a more accurate model using deep learning (DL) on electronic health record (EHR) data. We analyzed electronically abstracted data from 36,563 patients with diabetes and at least 1 hospitalization at an urban, academic medical center between 7/1/2010-12/31/2020. One hospitalization per patient was randomly selected for analysis. A DL, long short-term memory Recurrent Neural Network (RNN) was developed and compared to traditional models: LR, and Multi-layer Perceptron (MLP) . Models were developed using demographics, vitals, diagnostic and procedure codes, medications, laboratory tests, and administrative data as defined by the PCORnet Common Data Model. A look-back time of 1 year before the index hospitalization was used. Data dimensionality was reduced to 2,500 features by Singular Value Decomposition. The RNN C-statistic is significantly greater than that of the other models (Figure) . The RNN model outperforms the DERRI and is based on more generalizable EHR data. Varying the compression and time window might improve performance. RNN models may outperform traditional models at predicting readmission risk.

Disclosure
A.Abdel hai: None. M.Weiner: None. A.Paranjape: None. A.Livshits: None. J.Brown: None. Z.Obradovic: None. D.J.Rubin: Research Support; AstraZeneca.

Funding
National Institutes of Health (R01DK122073)
89-OR: Effective Real-World Usage of Artificial Intelligence–Based Decision Support System for Diabetes Management
Authors: NIMRI REVITAL , TIROSH AMIR , MULLER IDO , SHTRIT YAEL , PHILLIP MOSHE 
Published: May 2022

Background: Artificial intelligence-based decision support system (AI-DSS) for frequent insulin dose adjustment was demonstrated to be effective in improving glycemic control in our previous clinical trials. We aimed to assess the effectiveness of AI-DSS in real world diabetes clinics.
Methods: Data was collected and evaluated from routine AI-DSS usage for individuals with type 1 diabetes treated at 21 diabetes clinics (in the USA and 2 in Israel) . Two analyses were made: (1) Insulin dose recommendations were compared for rate of agreement/disagreement between those suggested by the AI-DSS and the approved recommendations by health care providers (HCP) . (2) Glycemic outcomes were compared between baseline and after 3 months of AI-DSS use. Included in this analysis were individuals who had mean baseline glucose level ≥ 182 mg/dL and at least one AI-DSS recommendation within the 3-months.
Results: A total of 8 recommendations, provided to 370 individuals, were evaluated. Full agreement on the direction of insulin dose adjustments was observed in 87%, 87%, and 83% of the basal rate, carbs ratio (CR) , and correction factor (CF) pump settings parameters, respectively. Full disagreement on the direction of dose change was observed in only 0.9%, 0.5%, and 1.2% for the basal rate, CR and CF, respectively. Glycemic outcome analysis included 1 eligible individuals (mean of 1.6 recommendations within a 3-month interval) . Average sensor percentage of time in range [70-180 mg/dL] increased by 4% (p=0.003) : 43% increased time in range by more than 5% and 33% increased by 10% or more. Time in hyperglycemia [&gt; 180 mg/dL] was reduced by 5% (p&lt;0.01) while time in severe hypoglycemia [&lt; 54 mg/dL] remained below 0.5%.
Conclusion: A high rate of agreement with automated insulin adjustments was observed among HCPs at academic centers who used the AI-DSS in their workflow. Glycemic control was significantly improved for sub-optimally controlled individuals already after 3-months of follow up.

Disclosure
R.Nimri: Employee; DreaMed Diabetes, Ltd., Research Support; Abbott Diabetes, Dexcom, Inc., Insulet Corporation, Medtronic, Speaker's Bureau; Eli Lilly and Company, Novo Nordisk, Stock/Shareholder; DreaMed Diabetes, Ltd. A.Tirosh: Advisory Panel; Abbott Diagnostics, AstraZeneca, Boehringer Ingelheim International GmbH, Merck & Co., Inc., Novo Nordisk, Sanofi, Consultant; Bayer AG, DreaMed Diabetes, Ltd., Research Support; Medtronic, Speaker's Bureau; Eli Lilly and Company. I.Muller: Board Member; DreaMed Diabetes, Ltd. Y.Shtrit: Employee; DreaMed Diabetes, Ltd. M.Phillip: Advisory Panel; Dompé, Insulet Corporation, Medtronic, Pfizer Inc., Board Member; DreaMed Diabetes, Ltd., Consultant; QuLab Medical Ltd., Other Relationship; Dexcom, Inc., Dompé, DreaMed Diabetes, Ltd., Eli Lilly and Company, Medtronic, NG Solutions Ltd, Novo Nordisk, OPKO, Pfizer Inc., Sanofi, Stock/Shareholder; DreaMed Diabetes, Ltd., NG Solutions Ltd.