Introduction and Objective: Fat and protein content of meals impact PPG excursion in individuals with Type 1 Diabetes (T1D). PPG management can be even more challenging in an FCL system with no premeal boluses. We explored how meal composition may impact glycemic outcomes when managing insulin in FCL. Methods: Thirty-four participants in 3 cohorts (adults, young adults, adolescents) with T1D (14-53 years old, 62% female, HbA1c 8.0±1.1%) tested the University of Virginia neural network-based system (AIDANET) during a five-day FCL study in supervised settings. Study staff recorded each meal's carbohydrate, protein, and fat content using food labels. Linear regression was used to determine the association between meal composition and PPG (4 hours following a meal) CGM metrics, adjusting for gender and age. Results: Overall, 301 meals were included in the analysis. The average carbohydrate, fat, and protein contents of the meals were 63.9 ± 34.8, 32.4 ± 23.3, and 32.6 ±17.5 grams, respectively. Unsurprisingly, larger carbohydrate amounts were associated with larger PPG excursions. There was no association between fat and protein content and the 4-hour incremental area under the curves (AUC). However, the protein content significantly reduced the AUC during the second hour after meal onset (Coefficient -45.53 mg/dl*min, p-value <0.002). AUC during the third and fourth hours after meal onset was significantly increased by fat content (Coefficient 32.57, 44.21, and p-values < 0.018 and 0.002, respectively). Fat also increased time to peak (Coefficient 0.57, p-value <0.003), while protein decreased peak PPG (Coefficient -0.66, p-value < 0.008). Time in range improved with more protein (Coefficient 0.003, p-value<0.025). However, time in tight range was not impacted by fat or protein. Conclusion: Meal composition significantly impacts FCL PPG profiles, and automatically accounting for its effects may further improve glycemic outcomes. L. Ekhlaspour: Other Relationship; Medtronic. Advisory Panel; Abbott, Medtronic. Consultant; Jaeb Center for Health Research. Research Support; MannKind Corporation. Speaker's Bureau; Insulet Corporation. Advisory Panel; Sequel Med Tech. Other Relationship; Tandem Diabetes Care, Inc. Research Support; Abbott. Other Relationship; Sanofi. J.Y. Hosseinipour: None. A. Narayan: None. Z.D. Perez: None. V. Holmes: None. M.D. Breton: Speaker's Bureau; Sinocare Inc, Tandem Diabetes Care, Inc. Consultant; Roche Diabetes Care, Boydsense. S.A. Brown: Research Support; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc, Tolerion, Roche Diabetes Care. Other Relationship; MannKind Corporation. G.P. Forlenza: Advisory Panel; Medtronic. Research Support; Medtronic, Dexcom, Inc. Consultant; Dexcom, Inc. Research Support; Insulet Corporation. Consultant; Insulet Corporation. Research Support; Tandem Diabetes Care, Inc. Advisory Panel; Tandem Diabetes Care, Inc. Research Support; Abbott. Advisory Panel; Sequel Med Tech. E. Cengiz: Advisory Panel; Novo Nordisk, Arecor Therapeutics, Eli Lilly and Company, Tandem Diabetes Care, Inc, Portal Insulin, MannKind Corporation. Breakthrough T1D
Introduction and Objective: Excess adiposity leads to insulin resistance, which augments cardiorenal risk in type 1 diabetes (T1D). Limited data exists regarding anti-obesity medication (AOM) prescribing practices in T1D despite obesity/overweight rates comparable to the general population. Our aim was to determine recent prevalence of elevated body mass index (BMI) and AOM use in a cohort of adults with T1D. Methods: This cross-sectional study examined Epic Cosmos electronic healthcare record data of adults, aged ≥ 18 years, with active problem list ICD-10 diagnosis for T1D (E10.x) and ≥ 2 face-to-face encounters in 2 year periods between 1/1/2017-12/31/2024. We queried prevalence of: 1) BMI 27 to <30 kg/m2, or ≥30 kg/m2, and 2) active AOM’s: phentermine, semaglutide, tirzepatide, liraglutide, orlistat, phentermine/topiramate, and naltrexone/bupropion. Results: In 2017, 20.1% had BMI 27 to <30 kg/m2 and 27.5% had BMI ≥ 30 kg/m2. By 2024, this increased to 22.9% and 33% respectively. AOM use increased from 1.9% (6,589 of 297,399) in 2017 to 13.9% (99,307 of 587,437) in 2024. While weekly incretin based treatments increased exponentially, other treatments were uncommon (Table). Conclusion: Overweight/obesity was common in this cohort. Although AOM use increased, a small portion of adults with BMI ≥ 27 kg/m2 are receiving treatment. Further study of anti-obesity medication safety and efficacy in people with T1D is needed. M. Sanchez: None. N. Reyes: None. A. Barros: None. A. Kinlaw: None. K. Love: None. National Institutes of Health (K23DK131327-01A1)
Introduction and Objective: The Automated Insulin Delivery as Adaptive NETwork (AIDANET) FCL adjusts its aggressiveness based on glycemic metrics computed from the last 14-days of CGM records, to adapt to the different insulin requirements that individuals may have. The system was tested in a broad cohort of people with T1D. We report changes in glycemic outcomes during home use. Methods: Participants enrolled in a randomized crossover trial across three age cohorts (14-17, 18-25 or 26-60 years) and two HbA1c groups (<8% or 8-12%). Depending on their 1:1 randomization, 2-week usual care data was collected either before or after completing a 5-day hotel stay for AID system training followed by the 7-day at-home period. AIDANET tunes control aggressiveness via a single parameter, defined as estimated Total Daily Insulin (eTDI), adjusted by a change factor (eTDIα) to minimize time below range 70 mg/dL (TBR) and time above range 180 mg/dL (TAR). eTDIα<1 implies a decreased eTDI. Results: Thirty-four participants completed the study. eTDI was reduced (eTDIα<1, Figure) when TBR increased and increased when reduced time in range (with increased TAR) was observed. TBR was reduced from start to end of the home period (-1.62%; p=0.048) while maintaining low TAR via the eTDI modulation. Conclusion: AIDANET adaptation system achieved reduced hypoglycemia by tailoring eTDI to each individual. M. Moscoso-Vasquez: Other Relationship; Dexcom, Inc. Research Support; Tandem Diabetes Care, Inc, National Institute of Diabetes and Digestive and Kidney Diseases. D. Flanagan: None. M. Clancy-Oliveri: None. E. Escobar: None. D. Fulkerson: None. G.P. Forlenza: Advisory Panel; Medtronic. Research Support; Medtronic, Dexcom, Inc. Consultant; Dexcom, Inc. Research Support; Insulet Corporation. Consultant; Insulet Corporation. Research Support; Tandem Diabetes Care, Inc. Advisory Panel; Tandem Diabetes Care, Inc. Research Support; Abbott. Advisory Panel; Sequel Med Tech. L. Ekhlaspour: Other Relationship; Medtronic. Advisory Panel; Abbott, Medtronic. Consultant; Jaeb Center for Health Research. Research Support; MannKind Corporation. Speaker's Bureau; Insulet Corporation. Advisory Panel; Sequel Med Tech. Other Relationship; Tandem Diabetes Care, Inc. Research Support; Abbott. Other Relationship; Sanofi. S.A. Brown: Research Support; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc, Tolerion, Roche Diabetes Care. Other Relationship; MannKind Corporation. M.D. Breton: Speaker's Bureau; Sinocare Inc, Tandem Diabetes Care, Inc. Consultant; Roche Diabetes Care, Boydsense. Breakthrough Type 1 Diabetes (2-SRA-2023-1275-M-B)
Introduction and Objective: The Automated Insulin Delivery as an Adaptive NETwork FCL system may improve glycemic outcomes while reducing burden by eliminating meal announcement. Continuous glucose monitoring (CGM) metrics were compared in users with T1D using the FCL system who had high or low baseline A1c. Methods: Youth and adults with T1D were enrolled in a randomized crossover study with a supervised hotel stay followed by 7 days of home use in FCL compared to usual care (UC). Half were selected with baseline A1c <8% (low A1c) and half with baseline A1c 8-12% (high A1c). CGM metrics were analyzed by A1c subgroups. Results: Thirty-four participants (25.4±12.6 years, 62% female) completed the study. Those with high A1c showed non-inferiority of FCL vs UC on all CGM metrics and lower mean glucose with FCL (Table). Time in 70-180 mg/dL, in 70-140 mg/dL, >180 mg/dL, and >250 mg/dL showed significant improvement with FCL in the high A1c group. Those with low A1c had statistically equivalent mean glucose and time <54 mg/dL with FCL vs UC; other CGM metrics were inconclusive. Conclusion: Use of a FCL system results in significant improvement in CGM metrics in those with less optimal glycemic management, while not deteriorating control in those with more optimal A1c. FCL systems have the potential to make the most impact in those with challenges in meeting glycemic goals. J.C. Wong: Research Support; Abbott, Dexcom, Inc., Tandem Diabetes Care, Inc. M. Moscoso-Vasquez: Other Relationship; Dexcom, Inc. Research Support; Tandem Diabetes Care, Inc, National Institute of Diabetes and Digestive and Kidney Diseases. L. Ekhlaspour: Other Relationship; Medtronic. Advisory Panel; Abbott, Medtronic. Consultant; Jaeb Center for Health Research. Research Support; MannKind Corporation. Speaker's Bureau; Insulet Corporation. Advisory Panel; Sequel Med Tech. Other Relationship; Tandem Diabetes Care, Inc. Research Support; Abbott. Other Relationship; Sanofi. S.A. Brown: Research Support; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc, Tolerion, Roche Diabetes Care. Other Relationship; MannKind Corporation. M.D. Breton: Speaker's Bureau; Sinocare Inc, Tandem Diabetes Care, Inc. Consultant; Roche Diabetes Care, Boydsense. G.P. Forlenza: Advisory Panel; Medtronic. Research Support; Medtronic, Dexcom, Inc. Consultant; Dexcom, Inc. Research Support; Insulet Corporation. Consultant; Insulet Corporation. Research Support; Tandem Diabetes Care, Inc. Advisory Panel; Tandem Diabetes Care, Inc. Research Support; Abbott. Advisory Panel; Sequel Med Tech. Breakthrough T1D
Introduction and Objective: Continuous glucose monitors (CGMs) and insulin pumps can optimize diabetes outcomes, yet their use in YA has been under-investigated, especially during the transition from pediatric to adult care. We explored patterns of diabetes device use at the start of this transition period. Methods: At baseline of a behavioral trial, 100 YA (age 17-25) with T1D reported on demographics, clinical variables, and measures of T1D self-management, diabetes distress, and quality of life. HbA1c was obtained from medical records or home kits. Device use was categorized as (1) CGM or pump or neither vs. (2) CGM and pump. T-tests and X-square tests compared device use groups. Results: As noted in Table, YA using more advanced technologies were more likely to live away from parents, attend post-high school, have private insurance, and have lower social vulnerability. They had lower HbA1c and reported less diabetes distress. Conclusion: At the cusp of transition to adult care, socioeconomic disparities in advanced device use across demographically diverse YA with T1D may be associated with differences in glycemic and psychosocial outcomes. Pediatric and adult clinicians should support sustained device use throughout transition. Policy change is needed to facilitate access to advanced devices. S.S. Eshtehardi: None. C.G. Minard: None. S.A. Carreon: None. S. Camey: None. W. Levy: None. S. Lyons: None. S. Mckay: None. S. Devaraj: None. R. Streisand: None. T.S. Tang: None. B.J. Anderson-Thomas: None. M.E. Hilliard: None. National Institute of Health (1R01DK119246)
