Last reviewed · How we verify
NCT07455643: CRUMB
Closed-loop Response to Unannounced Mixed and Carbohydrates-rich Breakfasts
NA trial testing Unannounced meal in Type 1 Diabetes Mellitus in 20 participants. Completed in 20 May 2025.
20 May 2025
Quick facts
| Lead sponsor | University of Catania |
|---|---|
| Phase | NA |
| Status | Completed |
| Study type | INTERVENTIONAL |
| Allocation | randomized |
| Design | crossover |
| Masking | none |
| Primary purpose | treatment |
| Enrollment | 20 |
| Start date | 20 April 2025 |
| Primary completion | 20 May 2025 |
| Estimated completion | 20 May 2025 |
| Sites | 1 location across Italy |
Drugs / interventions tested
- Unannounced meal
Conditions studied
- Type 1 Diabetes Mellitus — all drugs for Type 1 Diabetes Mellitus →
Sponsor
University of Catania
Who can join
Adults 11 to 18, any sex, with Type 1 Diabetes Mellitus. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
The development of advanced hybrid closed-loop (a-HCL) systems represents a significant step toward in improving glucose control and reducing user-dependent variability, especially In pediatric patients. Systems can automatically deliver correction boluses and modulate insulin delivery based on CGM feedback, thereby compensating for some of the consequences of human error. Current evidence suggests that a-HCL systems can tolerate unannounced carbohydrate loads up to approximately 20 g without compromising time in range (TIR) or safety. However, the metabolic response to larger or compositionally complex meals remains variable and highly dependent on the specific algorithm governing insulin delivery. Currently a variety of AID are commercially available: all of them present similarities and differences. The Medtronic MiniMed™ 780G uses a proportional-integral-derivative (PID) algorithm, a mathematical model that adjusts in real time insulin delivery rate based on 3 elements obtained from CGM reading values: the difference between the actual value and the chosen glucose target (proportional action), past values (integral action) and the glucose's rate of change (derivative action), In contrast, the Tandem t:slim X2™ with Control-IQ employs a model predictive control (MPC) algorithm, which aims, through a complex mathematical model, to predict glucose trends up to half-an-hour in the future, takin, also, in consideration actual and past glucose values. Despite sharing the same objective, said algorithms have different approaches, the former one being "reactive" and the latter "predictive". Therefore, their difference could result in different performances while facing mixed-nutrient meal or unannounced meals, defined as the consumptions of a meal with any prior insulin administration. Pediatric patients represent certainly a unique subgroup in which therapeutic adherence is a relevant issue, due to cognitive, developmental and behavioral factors. Understanding how different AID algorithms respond to unannounced meals in this age group is therefore crucial for optimizing safety and personalization of diabetes management. This study was designed to evaluate the strengths and limitations of two a-HCL systems, the Medtronic 780G (PID algorithm) and the Tandem t:slim X2 (MPC algorithm), in managing unannounced meals with different macronutrient compositions in children and adolescents with T1D. We also aim to better understand physiological and technological unannounced meal implications as to provide additional insight useful for the development of new fully closed loop algorithms, capable of minimizing glucose excursions and patient's burden.
Publications & conference data
No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.
Verify or expand the search:
- PubMed search for NCT07455643
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
- Google Scholar
Related trials
Other recruiting trials for Type 1 Diabetes Mellitus
Currently open trials in the same condition.
- NCT07142252 — Rezpegaldesleukin (NKTR-358) in New Onset Type 1 Diabetes Mellitus · Phase 2 · recruiting
- NCT07296276 — Accuracy and Precision of the Continuous Glucose Monitoring System 'CareSens Air 3' in Adult Patients With T1DM · NA · recruiting
- NCT07409701 — Gamification Intervertion in Children With Type 1 Diabetes · NA · recruiting
- NCT07356089 — Twiist Postmarket Surveillance Study for Type 1 Diabetes · recruiting
- NCT07434154 — Evaluation of Oxidative Stress: Comparison Between Type 1 Diabetes Mellitus and Latent Autoimmune Diabetes in Adults · recruiting
Other University of Catania trials
Trials by the same sponsor.
- NCT06831487 — Analysis of Line-Field Confocal Optical Coherence Tomography (LC-OCT) in Characterization of Gingival Systemic Health, G · NA · enrolling by invitation
- NCT06725095 — Effects of Enamel Matrix Derivative in the Treatment of Peri-implant Mucositis · NA · enrolling by invitation
- NCT06020937 — Olfactory and Trigeminal Functions in Patients With Multiple Sclerosis: Case-control Study · not yet recruiting
- NCT07300657 — Impact of an AI-Based Chatbot on Implant Patient Management: RCT · NA · completed
- NCT06382753 — Impact of Periodontal Supportive Therapy in Patients With Gingivitis and Periodontitis · recruiting
Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT07455643 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by University of Catania
- Last refreshed: 6 March 2026
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07455643.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing