Background/Objectives: Performing Hajj, the annual Islamic pilgrimage to Mecca and one of the world's largest mass gatherings, involves considerable physical exertion in high temperatures and presents unique challenges for people with type 1 diabetes (PWT1D). We examined the feasibility, safety, and user experience of automated insulin delivery (AID) systems during Hajj. Methods: This mixed-methods study evaluated six PWT1D who used an AID pump (2 MiniMed 780G, 2 Medtrum, 1 OmniPod 5, and 1 Open-source AID) while performing Hajj in 2024-2025. Pump and CGM-derived metrics were compared across pre-Hajj, during Hajj, and post-Hajj periods. A structured survey captured participants' experiences, challenges, and recommendations for AID use during Hajj. Results: The average percent time in range (TIR) remained stable from pre- to during Hajj (54.98 to 54.18, p > 0.05) and significantly increased post-Hajj (62.62, p < 0.05). The percent time above range (TAR > 180) and Glycemia Risk Index significantly decreased from pre- to post-Hajj (28.34 to 26.28 and 50.3 to 19.3, respectively, both p < 0.05). The percent time below range (TBR) remained low (<1%) across the three periods with no incidence of acute diabetes-related complications. Participants emphasized increased confidence and peace of mind with AID use and reported challenges related to heat exposure, prolonged walking, and lack of awareness regarding diabetes technology among HCPs. Conclusions: The use of AID during Hajj appeared to be safe and effective for PWT1D in our study, maintaining stable glycemic control under physically demanding conditions. As the first study to evaluate AID use during Hajj, our findings call for larger studies to explore the integration of diabetes technology into Hajj care protocols and highlight the need for structured pre-Hajj education for PWT1D and HCPs.

Introduction and Objective: Continuous subcutaneous insulin infusion (CSII) offers more physiological insulin delivery than multiple daily injections (MDI). Evidence on treatment satisfaction and fear of hypoglycemia (FOH) among people with type 1 diabetes (T1D) in Saudi Arabia remains limited. This study compared glycemic outcomes, treatment satisfaction, and FOH between CSII and MDI users. Methods: Adults with T1D using CSII or MDI at three diabetes clinics in Saudi Arabia completed the Diabetes Treatment Satisfaction Questionnaire (DTSQ) and Hypoglycemia Fear Survey-II (HFS-II). Hemoglobin A1c and time-in-range (TIR) were obtained from electronic records. Results: Among 238 participants (137 MDI, 101 CSII), mean age was 31.4±9.6 years and diabetes duration was 17.6±8.6 years. CSII users had lower A1c (7.01±1.01 vs. 8.16±1.37; p<0.001), higher TIR (66.7±17.8% vs. 51.5±16.8%; p<0.001), greater treatment satisfaction (33.1±4.7 vs. 28.0±7.8; p<0.001), and lower FOH scores (29.25±19.27 vs. 37.58±18.95; p=0.001). After adjustment, CSII use remained associated with higher satisfaction and lower perceived glycemic fluctuations. Conclusion: CSII therapy was associated with better glycemic control, higher treatment satisfaction, and lower FOH, supporting wider access to CSII in Saudi Arabia Disclosure S.A. Alamri: None. K.H. Aburisheh: None. Y. Alekrish: None. R.R. Aldrees: None. D.A. Alsagheir: None. M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi.

Introduction and Objective: Exposure to food advertising on social media has increased substantially in recent years. Yet its association with eating behaviors and glycemic outcomes among adults with type 1 diabetes (T1D) remains understudied. Here, we evaluated the association between exposure to food advertisements on social media, eating behaviors, and glycemic control-measured by hemoglobin AlC and continuous glucose monitoring (CGM)-derived time in range (TIR) among adults with T1D in Saudi Arabia. Methods: Adults with T1D using CGM and receiving care at a tertiary diabetes center in Riyadh, Saudi Arabia completed a structured questionnaire assessing social media use, frequency of exposure to food advertisements, and advertisement-related eating behaviors. Clinical data, including AlC and TIR were obtained from medical records. Participants were categorized into tertiles based on A1C and TIR. Multivariable logistic regression analyses were performed to identify factors associated with AlC above the sample median (>8.25%) and TIR below the sample median (<53%), after adjusting for potential confounders. Results: Higher exposure to food advertisements on social media was significantly associated with poorer glycemic control. Participants reporting near-daily exposure had higher AlC and lower TIR levels compared with those reporting minimal exposure (p<0.001). Reliance on food advertisements for meal selection, reported increased appetite following advertisement exposure, and advertisement-driven food purchasing were independently associated with increased odds of AlC >8.25% [OR = 54.9, 95% CI: 10.85-277.74, p <0.001] and TIR <53% % [OR = 65.86, 95% CI: 12.03-360.63, p <0.001]. In contrast, reliance on home-prepared food and higher educational level were inversely associated with poor glycemic outcomes. Conclusion: Among adults with T1D, frequent exposure to food advertising on social media is independently associated with adverse eating behaviors and poorer glycemic control. Disclosure K.H. Aburisheh: None. M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi. K.D. AlShahrani: None. N. Alshammari: None.

Introduction and Objective: Use of insulin and/or sulfonylurea (SU) during Ramadan fasting increases the dysglycemia risk among people with type 2 diabetes (PWT2D). We compared efficacy and safety profiles across 4 commonly used treatment regimens during Ramadan Methods: PWT2D were categorized into 4 groups based on insulin and SU use: Multiple daily injections (MDI; n=67), Combined Basal insulin (BI) and SU (BI+SU; n=30), BI alone (BI; n=23), and SU alone (SU; n=24). Concomitant use of other treatments (metformin, DPP-4i, and SGLT2i, etc) was permitted across all groups; and continuous glucose monitoring (CGM) was used to assess glycemic outcomes during Ramadan Results: During Ramadan, percent time in range (TIR) differed significantly across treatment groups: 46.90 (MDI), 59.98 (BI+SU), 60.53 (BI), and 64.55 (SU) (p=0.01). The Glycemia Risk Index (GRI) during Ramadan was highest in the MDI group (59.83), followed by BI+SU (40.66), BI (41.24), and SU (36.60) (p<0.01). The proportions of PWT2D achieving the “double CGM target” (TIR>70% and TBR<4%) for the MDI, BI+SU, BI, and SU groups were 20.97, 28.57, 40.91, and 54.55%, respectively (p=0.02). The proportions of PWT2D with a diabetes-related ER visit during Ramadan was highest in the MDI group (8.96%), followed by BI+SU (3.33%), and lowest in the BI (0%) and SU groups (0%) (p=0.17). The proportions of PWT2D who achieved the “fasting double target” (broke the fast because of diabetes ≤2 days and had Ramadan TIR>70%) were lowest in the MDI group (25.81%), followed by Basal+SU (28.57%) and BI (45.45%), and highest in the SU group (50%) (p=0.11). Users of SU alone and BI alone were 3.8 and 3.1 times likelier, respectively, to achieve the fasting double target compared with MDI users after adjusting for age, gender, employment/insurance status, educational level, age at diabetes diagnosis, and diabetes duration Conclusion: Use of MDI or combined BI and SU therapy in PWT2D during Ramadan was associated with poorer glycemic control and fasting experience compared with the use of BI or SU alone. Disclosure M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi. H. Albalawi: None. S.K. Alharthi: None. F. Alam: None. A.S. Alangri: None.

Introduction and Objective: Type 1 diabetes in adults is frequently misclassified as type 2, delaying appropriate insulin treatment and leading to worse outcomes. We developed a probability-based tool that combines pre-laboratory clinical characteristics and laboratory results to estimate the likelihood of type 1 vs. type 2 diabetes among adults with new-onset diabetes across four geographic regions. Methods: We convened an expert panel to design a web-based tool using demographic, anthropometric, and laboratory data. We used the published University of Exeter type 1/type 2 diabetes clinical features model for Western Europe and estimated analogous region-specific logistic regression models for Northern Europe (Scania ANDIS), Eastern Europe (Ukraine Exomes), and South Asia (Mohan Clinics). Pre-laboratory type 1 diabetes probability was modelled using log(age at onset), log(BMI), male sex, and parental history. Likelihood ratios for islet autoantibodies (glutamic acid decarboxylase autoantibodies, insulinoma-associated protein-2 autoantibodies, zinc transporter 8 autoantibodies, insulin autoantibodies) were derived from a systematic review and regional datasets. Fasting C-peptide measured at or near diagnosis was modelled with gamma distributions for type 1 and type 2 diabetes to provide continuous likelihood ratios. Results: Across 139,518 adults with new-onset diabetes, the proportion with type 1 diabetes ranged from 2.8% in South Asia to 13.2% in Western Europe. We implemented demographic, anthropometric, and laboratory data in a prototype web-based calculator that generates a pre-laboratory probability and subsequently updates it with autoantibody and C-peptide results. Conclusion: This is the first region-adapted calculator for estimating the probability of type 1 vs. type 2 diabetes in adults with new-onset diabetes. With continued validation in new datasets, this tool has the potential to improve diagnostic accuracy in diabetes and enable earlier appropriate treatment for adults with new-onset diabetes. Disclosure L.K. Billings: Advisory Panel; Current; Novo Nordisk, Lilly, Sanofi, Amgen Inc., Bayer AG. S. Misra: Other - Speaker honorarium for a single presentation at a conferences; Ended; A. Menarini Diagnostics, Lilly Diabetes. Advisory Panel; Ended; Insulet Corporation. Other - Speaker honoararium for a single presentation at a conferences; Ended; Sanofi. M.A. Kohn: None. O. Asplund: None. E. Ahlqvist: Research Support; Current; AstraZeneca. Other - Honorarium for lecture; Ended; AstraZeneca. V. Mohan: None. T.K. Oleksyk: None. M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi. J.M. Brix: Advisory Panel; Current; Abbott Diabetes, Boehringer Ingelheim International GmbH. Speaker's Bureau; Current; AstraZeneca. Speaker's Bureau; Ended; Dexcom, Inc. Speaker's Bureau; Current; Bayer AG. Advisory Panel; Current; Eli Lilly and Company, Merck Sharp & Dohme Corp. Speaker's Bureau; Ended; Medtronic. Advisory Panel; Current; Novo Nordisk. L. DiMeglio: Research Support; Ended; Dompé, Lilly. Stock/Shareholder; Ended; Lilly. Research Support; Current; MannKind Corporation. Research Support; Ended; Provention Bio, Inc. Research Support; Current; Sanofi. Research Support; Ended; Zealand Pharma A/S. Consultant; Current; Tandem Diabetes Care, Inc. Other - DSMB member; Current; Merck & Co., Inc., Lilly. K.L. Fantasia: Stock/Shareholder; Current; Eli Lilly and Company. D. Kerr: Stock/Shareholder; Current; Glooko, Inc. Research Support; Current; Abbott Diabetes. R. Ma: Research Support; Current; AstraZeneca. Speaker's Bureau; Ended; AstraZeneca. Research Support; Current; Boehringer Ingelheim International GmbH. Advisory Panel; Ended; Boehringer Ingelheim International GmbH. Research Support; Ended; Roche Diagnostics. Speaker's Bureau; Current; Roche Diagnostics. Speaker's Bureau; Ended; Eli Lilly and Company. Research Support; Ended; Novo Nordisk. Stock/Shareholder; Current; GemVCare Ltd. J.K. Mader: Research Support; Current; A. Menarini Diagnostics. Advisory Panel; Current; Abbott Diabetes. Speaker's Bureau; Current; Abbott Diabetes. Advisory Panel; Current; Becton, Dickinson and Company. Speaker's Bureau; Current; Becton, Dickinson and Company. Advisory Panel; Current; Insulet Corporation, Eli Lilly and Company. Speaker's Bureau; Current; Eli Lilly and Company. Advisory Panel; Current; Sanofi. Speaker's Bureau; Current; Sanofi. Advisory Panel; Current; Novo Nordisk A/S. Speaker's Bureau; Current; Novo Nordisk A/S. Advisory Panel; Current; Roche Diagnostics. Speaker's Bureau; Current; Roche Diagnostics. Advisory Panel; Current; Medtronic, Tandem Diabetes Care, Inc., Omnipod. Stock/Shareholder; Current; decide Clinical Software GmbH. Advisory Panel; Current; Dexcom, Inc. Speaker's Bureau; Current; Dexcom, Inc., Sinocare, Buzud. Advisory Panel; Current; Biomea Fusion, Pharmasens. Stock/Shareholder; Current; elyte Diagnostics. Other - CMO (unpaid); Current; elyte Diagnostics. Speaker's Bureau; Current; A. Menarini Diagnostics. Board Member; Current; OMNIA by AI APS. Advisory Panel; Current; Triple Jump. N. Mathioudakis: None. C. Mathieu: Advisory Panel; Current; Abbott Diagnostics, Dexcom, Inc. Board Member; Current; European Association for the Study of Diabetes. Advisory Panel; Current; Novo Nordisk, Eli Lilly and Company, Sanofi, Vertex Pharmaceuticals Incorporated, Medtronic. C. Mendez: None. Z. Quandt: Advisory Panel; Ended; Sanofi. M.J. Redondo: Advisory Panel; Current; Sanofi. Other - Data Safety Monitoring committee; Current; Lilly. E.D. Schleicher: None. V. Shah: Advisory Panel; Current; Abbott Diabetes, Dexcom, Inc. Advisory Panel; Ended; Medtronic. Advisory Panel; Current; Novo Nordisk, Eli Lilly and Company. Consultant; Current; Insulet Corporation, T1D Exchange. Advisory Panel; Current; Sanofi, Tandem Diabetes Care, Inc. Consultant; Ended; DreaMed Diabetes, Ltd. N. Thomas: Advisory Panel; Current; Sanofi. Other - Travel support; Ended; Sanofi. G. Umpierrez: Research Support; Current; Abbott, Dexcom, Inc., Bayer AG. Advisory Panel; Ended; Sanofi-Aventis U.S., Dexcom, Inc. Other - Education grant; Current; Lilly Diabetes, Abbott Diabetes. Advisory Panel; Current; Glycare, Glucotrack. Research Support; Current; Corcept Therapeutics. W. Wolfsberger: None. M. Shao: None. A.F. Scheideman: None. A.M. Zhou: None. A. Ayers: Consultant; Ended; Liom Health AG. D. Klonoff: Advisory Panel; Current; Afon Technology, Atropos Health, Embecta, Glooko, Inc., Glucotrack, Lifecare, Inc. Advisory Panel; Ended; Novo Nordisk. Advisory Panel; Current; Sanofi, Synchneuro, Thirdwayv Inc.

Introduction and Objective: The 2026 American Diabetes Association (ADA) Standards of Care do not incorporate pre-Ramadan continuous glucose monitoring (CGM) metrics into the Ramadan fasting risk calculator. We examined changes in CGM metrics from pre-Ramadan to during Ramadan among people with type 2 diabetes (PWT2D) and identified pre-Ramadan CGM predictors of fasting experience and glycemic control during Ramadan Methods: PWT2D were categorized into 2 groups based on achieving the fasting double target, defined as breaking the fast because of diabetes on ≤2 days and achieving a Ramadan time in range (TIR) of >70%. Pre-Ramadan CGM metrics were compared between the two groups. Changes in CGM metrics from pre-Ramadan to Ramadan were examined across 4 treatment regimens: multiple daily injections (MDI), basal insulin (BI), BI and sulfonylurea (BI+SU); and (SU). Logistic regression was used to identify pre-Ramadan CGM predictors of achieving the fasting double target Results: Percent time in range (TIR) decreased from pre-Ramadan to Ramadan across all treatment groups: MDI (52.22 to 46.83, p<0.05); BI+SU (69.21 to 61.26, ); BI (66.14 to 59.32, p>0.05); and SU (71.08 to 64.55, p<0.05). Similarly, glycemia risk index (GRI) increased during Ramadan: MDI (51.65 to 59.78, p<0.05); BI+SU (31.50 to 40.11, p<0.05); BI (33.91 to 44.52, p>0.05); and SU (30.05 to 36.60, p<0.05), driven primarily by hyperglycemia. After adjusting for age, gender, employment/insurance status, educational level, age at diabetes diagnosis, and diabetes duration, the following pre-Ramadan CGM metrics independently predicted achievement of the fasting double target: TIR70-180>70%, TBR<54 >1%, TAR>180 <25%, TAR>250 <5%, and GRI<40 Conclusion: Pre-Ramadan CGM metrics are clinically meaningful predictors of fasting experience and glycemic control during Ramadan. Incorporating these metrics into the ADA Ramadan fasting risk calculator may improve risk stratification and individualized counseling for PWT2D who plan to fast Disclosure M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi. S.K. Alharthi: None. H. Albalawi: None. A.S. Alangri: None. F. Alam: None.

Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH's Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article's cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH's GSS principles.

Artificial Intelligence (AI) has the potential to impact healthcare across multiple domains. In diabetes, a complex chronic disease affecting 600 million people globally, AI is already being used from primary care to tertiary specialist care to reduce patient and clinician burden. However, for medical AI to be widely implemented and applied specifically to diabetes, such stakeholders as patients, clinicians, healthcare administrators, regulators, and AI developers will need to establish trust in this technology. Building trust is a balancing act depending on individual priorities of stakeholders which may not necessarily align. Both probabilistic outputs and “top-choice only” outputs are used in medical AI. To achieve trust in AI for diabetes care, it will be necessary to move beyond expecting only single, deterministic outputs and to establish clear standards for medical AI provenance and performance. This article presents priorities for each of the various stakeholders if they are to develop trust in medical AI and their responsibilities for contributing to the establishment of trust in medical AI. For a medical AI system to be trustworthy, six key attributes must be incorporated including accuracy, reproducibility, privacy/security, transparency, human oversight, and fairness. We present practical methods to achieve each of these six attributes of trustworthy medical AI prioritizing diabetes that are important for all stakeholders.

BACKGROUND:Many people with type 2 diabetes (PWT2D) using insulin are advised not to fast during Ramadan because of dysglycemia risk. Here, we examined the efficacy and safety of using a smartphone application to titrate basal insulin doses to fasting blood glucose (FBG) target 70-130 mg/dL in PWT2D who practiced Ramadan fasting. METHODS:We retrospectively analysed data from 106 PWT2D using basal insulin and My Dose Coach (MDC) who fasted during Ramadan 2022-2024 and had FBG data pre-, during, and after Ramadan. Per standard of care, those with pre-Ramadan FBG < 130 mg/dL had basal insulin doses reduced by 20% (Reduced Basal 'RB' Group). Those with pre-Ramadan FBG ≥ 130 mg/dL continued usual doses (Usual Basal 'UB' Group). During Ramadan, MDC guided basal insulin titration based on pre-sunset FBG. RESULTS:Average FBG levels in the RB and UB groups were: pre-Ramadan (120 vs. 145 mg/dL, p < 0.01), week-1 (129 vs. 139 mg/dL, p = 0.14), week-2 (131 vs. 141 mg/dL, p = 0.13), week-3 (130 vs. 138 mg/dL, p = 0.07), week-4 (129 vs. 138 mg/dL, p = 0.11), and post-Ramadan (126 and 138 mg/dL, p = 0.03). In the RB group, the MDC app increased the average basal insulin dose from 29 units in Ramadan Week 1 to 33 units by Week 4; similarly, in the UB group, the mean dose increased from 34 to 38 units. The proportion of PWT2D with FBG 70-130 mg/dL was maintained throughout Ramadan in both groups with no documented FBG < 70 mg/dL. CONCLUSION:Use of smartphone-based automated basal insulin titration during Ramadan fasting is effective in maintaining FBG levels within target in PWT2D.

A panel of experts in the use of continuous glucose monitoring (CGM) data in the treatment of diabetes met in Burlingame, California on October 27, 2025 to discuss the utility of the glycemia risk index (GRI) for clinical care research and population health management. The GRI composite metric is a single number (on a 0-100 percentile scale-lower is better) based on an expert-determined weighting of the seven individual components in the existing ambulatory glucose profile (AGP). The GRI describes the quality of glycemia based on glucose values collected in a 14-day CGM tracing, thus providing additional insights into CGM profiles beyond the AGP. During the meeting, the mathematical derivation of the GRI metric was presented along with its use for adult and pediatric individuals with diabetes and cancer who require medications that can adversely affect the glucose concentration. Examples where the GRI provided useful insights into the quality of CGM tracings were also discussed by the expert panel. In addition, a new smartphone application, the GRI Calculator, was presented. This app calculates the GRI of a CGM tracing and provides visualization of sequential CGM tracings for a specific individual. The GRI provides a reference measurement for the accuracy of artificial intelligence (AI) models assigning levels of glycemic quality to CGM tracings intended to match the assessments of clinicians. The GRI is now part of the data visualization panel for the Integration of Connected Diabetes Device Data into the Electronic Health Record (iCoDE-2) project, which standardizes both CGM and insulin dosing data. Further exploration of the potential value of the GRI for non-insulin users needs to be undertaken. The panel unanimously recommended that CGM manufacturers and developers of data visualization software for CGMs add the GRI to their data platforms for insulin users.

Introduction and Objective: Automated insulin delivery (AID) systems improve glycemic control in people with type 1 diabetes (PWT1D). Despite these benefits, access to AID systems in private healthcare settings is often limited by insurance coverage decisions. Here, we examined the impact of insurance coverage decisions for insulin pump therapy on clinical outcomes among PWT1D in Saudi Arabia Methods: A retrospective analysis of clinical data from 492 insured PWT1D who were prescribed an AID system by their treating endocrinologist at a private diabetes center in Saudi Arabia between January 2022 and February 2025 Results: Overall, 55% of participants were female. Insurance approval was granted for 44.1% of insulin pump requests, while 56% were denied. Participants whose pump requests were approved were younger than those denied (median age: 18 vs 26 years, p<0.01), had shorter diabetes duration (7.5 vs 11 years, p<0.01), and had greater baseline hemoglobin A1C levels (8.7 % vs 8.1, p<0.01). Among PWT1D with insurance approval who initiated AID therapy, hemoglobin A1C levels improved from baseline through months 3, 6, 12, and 16 (8.91, 7.57, 7.45, 7.86, 7.61%, respectively, overall p<0.05). In contrast, PWT1D whose pump requests were denied, and who did not initiate AID therapy, continued to have above-target A1C levels throughout follow-up (baseline to months 3, 6, 12, and 16: 8.62, 8.16, 8.02, 8.44, and 8.44%, respectively; overall p>0.05). After adjusting for age, gender, age at time of T1D diagnosis, baseline A1C, and baseline modality of T1D treatment, the likelihood of achieving an A1C level of <7% was significantly higher in the insurance-approved group compared to the denied group at both month 3 (OR: 4.22) and month 16 (OR:3.20) Conclusion: Insurance coverage decisions for AID therapy play a critical role in shaping long-term glycemic outcomes. Ensuring adequate insurance coverage for AID may improve clinical outcomes and help reduce the long-term financial burden on the healthcare system Disclosure M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi. Y.A. Alzamil: None.

Introduction and Objective: Ramadan fasting is associated with dysglycemia risk in people living with diabetes (PLWD). This real-world study evaluated glycemic management in PLWD before, during, and after Ramadan fasting using continuous glucose monitoring (CGM). Methods: Retrospective analysis of Dexcom CGM users (≥18 years) living in an Arab Gulf country, Jordan, or Lebanon, and self-reported living with diabetes and fasted during Ramadan at least once between 2020-2025 (confirmed via survey). Outcomes were change in CGM metrics between the month before, during, and after Ramadan. Subgroup analyses (e.g., diabetes type, insulin therapy, age < or ≥60 years, gender) were conducted. Results: A total of 103 participants completed the survey and met the inclusion criteria for analysis (70% CGM utilization during all periods) and were included in the analysis (see Table for demographics). Mean Level 1 hypoglycemic events per week significantly decreased during Ramadan compared to before and after (p<0.05, Table). No other significant CGM metric differences were observed. Insulin therapy subgroup analysis showed multiple daily injection users observed significantly higher CV and rebound hyperglycemic events per week during all periods (data not shown). Conclusion: In this real-world, retrospective study of PLWD, glycemic management before, during, and after Ramadan fasting with use of CGM was maintained. Disclosure M.M. Hassanein: None. R.M. Alamoudi: None. J. Tomlin: Employee; Current; Dexcom, Inc. H. Yousri: None. C. Chen: Employee; Current; Dexcom, Inc., Google. Y. Xu: Employee; Current; Dexcom, Inc. L. Yang: Employee; Current; Dexcom, Inc. S. Jones: Employee; Current; Dexcom, Inc. C. Griffen: Employee; Current; Dexcom, Inc. M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi.

Introduction and Objective: Although people with type 1 diabetes (PWT1D) are exempt from fasting; most choose to fast during Ramadan for religious, emotional, and social reasons. While Ramadan clinical guidelines exist, the patient perspectives and priorities remain underrepresented in these guidelines. Here, we reported the lived experiences, priorities, and challenges of PWT1D during Ramadan fasting Methods: A mixed-methods qualitative focus group workshop was conducted with 16 PWT1D from across Saudi Arabia. Semi-structured discussions explored fasting goals, fears, barriers, facilitators, and the role of digital health. Audio recordings were transcribed, translated, and analyzed using an inductive reflexive thematic analysis. Three researchers independently conducted open coding, followed by iterative development of a coding framework and refinement of themes through consensus Results: Mean age of the study participants and duration of diabetes were 26 and 13 years, respectively. Overall, 93% reported fasting during the last Ramadan. Fasting decisions were shaped by perceived religious commitment, family influence, prior experience, social pressure, and medical advice. Key challenges included post-iftar hyperglycemia, fear of daytime hypoglycemia, risk of diabetic ketoacidosis, fatigue, psychological burden, and embarrassment/guilt when breaking the fast. Although CGMs and insulin pumps have facilitated safer fasting, they did not replace individualized preparation, education, and self-management skills. Pre-Ramadan counseling, family support, and clinician guidance remain key facilitators Conclusion: Ramadan fasting for PWT1D involves complex medical, psychosocial, and religious considerations. Patient priorities extend beyond glycemic control to include other health factors, spiritual fulfillment, and social inclusion. Integrating patient perspectives and priorities into pre-Ramadan care and guidelines may support safer and patient-centered fasting experience Disclosure M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi. L.A. Alqarni: None. B. Alsalmi: None.

Introduction and Objective: Over 116 million Muslims with diabetes fast during Ramadan each year, placing them at risk of dysglycemia and acute complications. Current pre-Ramadan risk assessment relies largely on complex expert consensus-based tools without quantitative predictive tools. We aimed to develop and internally validate the first CGM-based model to predict fasting glycemic complications Methods: We analyzed data from 550 individuals with diabetes who fasted during Ramadan and used CGM during Ramadan and the preceding month. Candidate predictors included demographic and clinical variables and pre-Ramadan CGM metrics: time in range (TIR), time below range (TBR), glycemia risk index (GRI), and coefficient of variation (CV). The primary outcome was fasting glycemic complications during Ramadan: defined as DKA, an ED visit, fasting interruption on >2 days due to dysglycemia, or Ramadan TIR <70%. A multivariable logistic regression model was developed. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC), and internal validation used 10-fold cross-validation Results: The median age was 27 years [IQR 19-42]; 60.0% were female; 77.1% had type 1 diabetes. Overall, 410 (74.5%) experienced fasting glycemic complications. Lower pre-Ramadan TIR, higher GRI, diabetes treatment modality, and longer diabetes duration were strong predictors of fasting glycemic complications. A prediction model based solely on pre-Ramadan CGM metrics demonstrated excellent discrimination (AUC of 0.91, 95% CI 0.89-0.94) and maintained performance after internal validation (cross-validated AUC of 0.89; 95% CI 0.87-0.92), comparable to models incorporating an extensive list of demographic and clinical variables Conclusion: This innovative CGM-based prediction model identifies PWD at risk of fasting glycemic complications and provides a simple, scalable, and practical tool for real-world risk stratification and personalized counseling. Disclosure M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi. S.K. Alharthi: None. M. Abusamaan: None. S.A. Meo: None. D. Klonoff: Advisory Panel; Current; Afon Technology, Atropos Health, Embecta, Glooko, Inc., Glucotrack, Lifecare, Inc. Advisory Panel; Ended; Novo Nordisk. Advisory Panel; Current; Sanofi, Synchneuro, Thirdwayv Inc.

Introduction and Objective: Use of continuous glucose monitoring (CGM) systems during Ramadan fasting is increasing among people with type 2 diabetes (PWT2D). We evaluated changes in CGM metrics from pre- to during Ramadan and whether pre-Ramadan CGM metrics predicted fasting success among high-risk PWT2D who attempted to fast while using the SYAI CGM sensor. Methods: Pre-Ramadan and Ramadan CGM data were reviewed for 64 PWT2D using insulin and/or sulfonylurea (SU) who attempted to fast during Ramadan 2025. Eligible participants had two months of CGM data (pre- and during Ramadan) and sensor active time ≥70%. A composite “fasting double target” was defined as breaking the fast because of diabetes on ≤2 days and achieving Ramadan TIR >70%. Logistic regression was used to identify predictors of achieving the fasting double target Results: Glycemic control worsened from pre- to during Ramadan as follows: time in range (TIR) (67.68 to 62.38%, p<0.01); time above range (TAR>250) (8.57 to 11.18, p=0.01); Glycemia risk index (GRI) (32.82 to 39.49, p<0.01), and average glucose (165.85 to 173 mg/dl, p<0.01). No significant changes were noted in TBR, TAR>180, or coefficient of variation. Only 45% of PWT2D achieved the fasting double target. Participants who achieved the fasting double target had better pre-Ramadan CGM metrics than those who did not (TIR: 83.68 vs 54.58, p<0.01; GRI: 16.25 vs 46.39, p<0.01; average glucose: 141.63 vs 185.67, p<0.01; GMI: 6.72 vs 7.95, p<0.01; and TAR>180: 13.66 vs 32.48, p<0.01; and TAR>250: 1.62 vs 14.25, p<0.01). After adjusting for age, sex, employment/insurance status, educational level, age at diabetes diagnosis, and diabetes duration; pre-Ramadan TIR>70% and GRI ≤20 were strongly associated with achieving the fasting double target Conclusion: Among high-risk PWT2D using insulin and/or SU, glycemic control worsened during Ramadan fasting. Pre-Ramadan TIR and GRI were strongly associated with successful fasting, highlighting the importance of CGM-guided risk stratification and counseling before Ramadan Disclosure M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi. H. Albalawi: None. S.K. Alharthi: None. F. Alam: None. A.S. Alangri: None. K.H. Aburisheh: None.

Introduction and Objective: Rapid advances in diabetes technology have increased the educational burden on healthcare professionals (HCPs); while structured training on the clinical use of insulin pumps and continuous glucose monitoring (CGM) systems remains limited. We evaluated the effectiveness of a five-year series of diabetes technology workshops, in which accumulated experience informed the design of a final workshop adopting a progressive scaffolded learning model Methods: Between 2021-2025, 6 diabetes technology workshops were conducted and iteratively refined based on participant feedback and accumulated experience. Workshop #6 represented the most advanced iteration and was designed using a Progressive, Scaffolded Learning Model comprising 4 stages: building the knowledge base, exploring the tools, establishing a hands-on foundation, and applying skills through real-world clinical scenarios. Workshop effectiveness was evaluated using Kirkpatrick’s Four-Level Model : assessing participant reaction, learning, behavior change, and perceived results through pre- and post-workshop surveys and a 2-month follow-up Results: 195 participants completed the evaluation surveys. Across all workshops, participants’ confidence in using pumps and CGMs increased remarkably from pre- to post-workshop. At 2-month follow-up, 93% of respondents reported applying workshop learning in daily clinical practice, 84% shared acquired knowledge with colleagues, and 89% reported better knowledge retention from hands-on training compared with traditional lectures. In Workshop #6, substantial improvements were observed in self-reported confidence related to pump initiation, optimization of pump settings, and interpretation of pump/CGM reports Conclusion: A Progressive, Scaffolded Learning Model effectively enhances confidence, skill acquisition, and real-world application of diabetes technology among HCPs. Our approach provides a scalable framework for diabetes technology education and may inform future training programs Disclosure M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi. J.M. Abuhaimed: None.

Introduction and Objective: Continuous glucose monitoring (CGMs) is the standard of care for people with type 1 diabetes (PWT1D), yet studies linking CGM metrics with diabetes complications in PWT1D remain limited. We examined the association between CGM metrics and microvascular complications in PWT1D Methods: Clinical and CGM data from 370 PWT1D were analyzed across 5 clinics in Saudi Arabia. Participants with available retinal exams, one-month CGM data within 3 months of the retinal exam, and sensor active time ≥70% were included. Risk factors of diabetic retinopathy (DR), albuminuria, and a composite microvascular complication (CMC) of DR and/or albuminuria were assessed using multivariable logistic regression Results: Participants with TIR>50% had lower rates of DR (20 vs 29.10%, p=0.04), albuminuria (10.9 vs 23.4%, p<0.01), and CMC (29.1 vs 42.9%, p<0.01) than those with TIR≤50. Those with TIR>70% had lower rates of DR (19.7 vs 25.2%, p=0.32), albuminuria (9.8 vs 18.2%, p=0.11), and CMC (28.1 vs 37.2%, p=0.17) than those with TIR≤70%. Increasing glycemia risk index (GRI) categories (0-40, 41-60, 61-80, and >80) corresponded with higher rates of DR (18, 21, 28, and 29%, p=0.27), albuminuria (11, 7, 25, and 24%, p<0.01), and CMC (28, 27, 43, and 44%, p=0.03). Diabetes duration≥10 years, TIR≤60%, and GRI>80 were significant predictors of retinopathy after adjusting for age, sex, diabetes duration, TIR, GRI, A1C, systolic blood pressure (SBP), triglycerides, and HDL as appropriate. For albuminuria, the significant predictors in the adjusted model were diabetes duration ≥10 years, SBP>124 mmHg, and TG>1.12. Hemoglobin A1C was not independently associated with microvascular complications in the adjusted models. Conclusion: Lower TIR and higher GRI, SBP, and TG independently predict microvascular complications, while hemoglobin A1C is not independently associated with microvascular outcomes among PWT1D in Saudi Arabia. These findings highlight the added clinical value of CGM-derived metrics beyond A1C alone. Disclosure M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi. R. Alshareef: None. A.A. Alhumaizi: None. A. Shadid: None. A. Shadid: None. M. Makkawi: None. A. Albacker: None. O. Aldosari: None. M.A. Batais: None. T.H. Almigbal: None.

Approximately one fifth of the population of Saudi Arabia had obesity in 2019. Due to the considerable personal and societal cost of obesity in Saudi Arabia, a national initiative to reduce the prevalence of obesity is underway. This study describes the characteristics of people with obesity (PwO) and the impact of obesity on spending, work productivity and health-related quality of life (HRQoL) in Saudi Arabia to help further advance the management of overweight and obesity in this country. Data were from the multinational cross-sectional survey Adelphi Obesity Disease Specific Programme™. Physicians responsible for obesity management decisions completed online questionnaires for up to five consecutive qualifying adult PwO attending their clinic. PwO with physician-completed forms were invited to complete a separate questionnaire. Analyses were descriptive. Overall, 40 physicians provided physician-completed forms for 200 PwO; of these, 90 PwO completed a self-report form. For the 200 PwO, mean time since diagnosis was 23.0 months. A mean of 1.6 comorbidities were reported, most commonly hypothyroidism, depression and type 2 diabetes. Current weight loss approaches included diet, exercise and medication. Other than weight loss, improving HRQoL of PwO was the most common goal reported by physicians (for 70.0

INTRODUCTION:Most people with type 1 diabetes (PWT1D) typically avoid fasting during Ramadan because of dysglycemia risks. We compared efficacy, safety profiles, and fear of hypoglycemia (FOH) during Ramadan fasting in users of four insulin delivery modalities. METHODS:We compared four treatment groups: automated insulin delivery (AID) (n = 114), sensor augmented pump with predictive-low-glucose suspend (SAP-PLGS) (n = 4), sensor-unintegrated pump (SUP) (n = 24), and multiple daily injections (MDI) + continuous glucose monitoring (CGM) (n = 136). RESULTS:Pre/during-Ramadan mean percent time in range for the AID, SAP-PLGS, SUP, and MDI-CGM groups was 73.2/73.4, 62/65.5, 57.8/54.6, and 52.1/47.4. The pre/during-Ramadan Glycemia Risk Index for the AID, SAP-PLGS, SUP, and MDI-CGM groups was 30/29, 43/35, 52/55, and 60/64. The proportion of PWT1D achieving a "double target of fasting" (broken fasting ≤ 2 days of Ramadan because of diabetes and TIR > 70%) for the AID, SAP-PLGS, SUP, and MDI-CGM groups was 46.5%, 25%, 12.5%, and 7.35%. AID system users, versus MDI + CGM users, were 22 times likelier to achieve the double target after adjusting for age, sex, employment/insurance status, educational level, and diabetes duration. While the overall FOH score did not significantly differ across the four groups (p > 0.05), AID users had the lowest score and MDI had the highest score (1.23 and 1.63, respectively, p < 0.01) on the survey's behavior subscale. CONCLUSION:Use of AID during Ramadan fasting was associated with fasting the most days of Ramadan, best glycemic control, and the least frequent hypoglycemia avoidance behaviors. The International Diabetes Federation/Diabetes and Ramadan International Alliance risk calculator should consider a lower risk score for AID technology when used by PWT1D.

Introduction and Objective: Diabetic ketoacidosis (DKA) remains a common presentation of type 1 diabetes (T1D) and may reflect delayed diagnosis and greater metabolic decompensation. Whether DKA at diagnosis influences the early clinical trajectory of T1D remains unclear. We evaluated the association between DKA at diagnosis and clinical outcomes during the first three years after T1D diagnosis Methods: We conducted a retrospective cohort study of 160 individuals with newly diagnosed T1D followed at a tertiary diabetes center in Riyadh, Saudi Arabia. Participants were categorized by the presence or absence of DKA at diagnosis. Clinical characteristics and longitudinal outcomes were compared between groups over approximately three years of follow-up Results: Among 160 participants, 76 (47.5%) presented with DKA at diagnosis. Baseline age, age at time of diagnosis, sex, and A1C at the first visit in our clinic were similar between those with and without DKA at diagnosis. Individuals with DKA had higher urinary albumin-to-creatinine ratio (ACR) during their first visit in our clinic (191 vs 57 mg/g, p=0.04) and reported more frequent severe hypoglycemia (17.9% vs 4.6%, p=0.02). Overall A1C improved from 9.4% to 8.1% during follow-up. However, individuals with DKA at diagnosis maintained slightly higher A1C at the last visit compared with those without DKA (8.3% vs 8.0%). Urinary ACR also remained higher at the last visit in those with DKA at diagnosis (253 mg/g vs 101 mg/g). Conclusion: DKA at diagnosis was associated with higher baseline and follow up albuminuria, greater frequency of severe hypoglycemia, and slightly higher A1C during follow-up, suggesting a more vulnerable early clinical trajectory and highlighting the need for targeted follow-up and early risk stratification after T1D diagnosis Disclosure A.A. Alelaiwi: None. A. Alnasser: None. A.A. Badahdah: None. S.K. Alharthi: None. M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi.