Closed-loop Response to Unannounced Mixed and Carbohydrates-rich Breakfasts

NCT07455643 | Completed

• 1 other identifier: CRUMB Study
• Study Type: interventional
• Target: 20
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Type 1 Diabetes Mellitus

Keywords

type 1 diabetes mellitus; automated insulin delivery; unannounced meals; proportional integral derivative control; model predictive control

Outcome Measures

Primary Outcomes (1)

• Change in blood glucose concentration from pre-meal to two hours post-meal (2h-ΔBG), as measured by CGM in mg/dl

  Change in blood glucose concentration from pre-meal to two hours post-meal (2h-ΔBG), as measured by CGM

  from pre-meal to two hours post-meal

Secondary Outcomes (8)

• Pre- and post-meal four-hour glucose difference (4h-ΔBG), as measured by CGM in mg/dl

  from pre-meal to four hour postprandial

• Peak CGM value in mg/dl

  from pre-meal to four hour postprandial

• Time to peak CGM value

  from pre-meal to four hour postprandial

• Percentage of spent time in range (TIR, 70-180 mg/dL), as measured by CGM

  from pre-meal to four hour postprandial

• Percentage of spent time in tight range (TITR, 70-140 mg/dL), as measured by CGM

  from pre-meal to four hour postprandial

Study Arms (2)

Announced meal (AM)

NO INTERVENTION

Participants followed their usual therapy while consuming both the "CHO meal" and the "mixed meal" three times each. In this phase, the carbohydrate content of each meal was announced to the insulin pump, and pre-meal boluses were delivered as per standard practice.

Unannounced meal (UM)

EXPERIMENTAL

Participants consumed the same meals, an equal number of times, without announcing their carbohydrate intake to the device. Consequently, no user-initiated boluses were administered during this period, and all insulin delivery adjustments were determined exclusively by the algorithm.

Other: Unannounced meal

Interventions

Unannounced meal OTHER

Participants consumed both the "CHO meal" and the "mixed meal" three times each, without announcing their carbohydrate intake to the device. Consequently, no user-initiated boluses were administered during this period, and all insulin delivery adjustments were determined exclusively by the algorithm.

Unannounced meal (UM)

Eligibility Criteria

• Age: 11 Years - 18 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64)

You may qualify if:

• Age between 11 and 18 years;
• Type 1 diabetes diagnosed for at least 12 months;
• Use of an A-HCL device for at least 3 months;
• Proficiency in carbohydrate counting.

You may not qualify if:

• Presence of malabsorption disorders;
• Presence of chronic diabetes complications.

Sponsors & Collaborators

• University of Catania: lead

Study Sites (1)

Azienda Ospedaliero Universitaria Policlinico "G.Rodolico - San Marco" - Catania

Catania, Sicily, 95123, Italy

Location

MeSH Terms

Conditions

Diabetes Mellitus, Type 1

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Autoimmune Diseases; Immune System Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: February 23, 2026
• First Posted: March 6, 2026
• Study Start: April 20, 2025
• Primary Completion: May 20, 2025
• Study Completion: May 20, 2025
• Last Updated: March 6, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)