CGM Boosted by Artificial Intelligence for Better Glycaemic Control in T2 Diabetes

CGM Boosted by Artificial Intelligence for Better Glycaemic Control in T2 Diabetes: a Real-life Multicentre Observational Study in Belgium.

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07064499

Acronym

MAGIC-T2D

Enrollment

440

Registered

2025-07-14

Start date

2023-12-15

Completion date

2027-07-31

Last updated

2025-08-11

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Type 2 Diabetes

Keywords

Evolution of Type 2 Diabetes Following CGM Introduction, Progression of Type 2 Diabetes Across Endotypes Following CGM Initiation

Brief summary

Title: CGM Boosted by Artificial Intelligence for better glycaemic control in T2 Diabetes : a real-life multicenter observational study in Belgium - MAGIC-T2D Background: In Belgium, since May 2023, reimbursement for continuous glucose monitoring systems has been extended to people with type 2 diabetes taking multiple injections of insulin. Despite the results of previous studies, the impact of this technology in terms of glycaemic control and patient-reported outcomes (PROMs) in real life for patients with type 2 diabetes is still unclear. Objective: To evaluate the impact of the CGMs on glycemic control and PROMs in people living with type 2 diabetes under real-life conditions. Methods and analysis: In a multicenter real-world observational study, more than 440 people with type 2 diabetes under multiple daily injection therapy who start to be monitored by a CGM in one of the 13 participating Belgian centers, will be followed for a period of 24 months. Outcomes: The primary and secondary endpoint are respectively the evolution of time spent in range (defined as a sensor glucose value between 70 and 180 mg/dL) and HbA1c from before start to 12 months after start of the CGM. Exploratory objectives will be studied using artificial intelligence (AI) to study glucose profiles to characterize the heterogeneity of type 2 diabetes.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

* Type 2 diabetes on MDI and starting CGM

Exclusion criteria

* Dialysis * Treatment that raises blood sugar levels (corticosteroids, etc.) * Secondary and type 1 diabetes * Patient not able to sign the Informed Consent

Design outcomes

Primary

Measure	Time frame
Evolution of TIR (glucose 70-180 mg/dl) over a 12-month period after initiation of CGM in patients with T2D treated with MDI regimens.	From registration to end of 1-year follow-up

Secondary

Measure	Time frame
Evolution of Hba1c over a 12-month period after initiation of CGM	From registration to end of 1-year follow-up

Other

Measure	Time frame
Evolution of TIR according to T2D endotypes as defined by Ahlqvist et al.	From registration to end of 1-year follow-up

Countries

Belgium

Outcome results