SAT-625 Democratization of Glycemic Outcomes in People with Type 1 Diabetes (T1D) using the MiniMed™ 780G System of Automated Insulin Delivery (AID)
Bibliographic record
Abstract
Abstract Disclosure: R.A. Vigersky: Medtronic Minimed. T. Cordero: Medtronic Diabetes. A. Arrieta: Medtronic Diabetes. M. Liu: Medtronic Diabetes. B. Grosman: Medtronic Diabetes. J. Shin: Medtronic Diabetes. Introduction: AID therapy is recognized as the standard of care for managing glycemia in children and adults with T1D by professional societies and health ministries, globally (ADA- 2025. doi:10.2337/dc25-S007; ISPAD- 2024. doi:10.1159/000543034; UK NICE- https://www.nice.org.uk/guidance/TA943). There are, currently, six commercialized AID therapies (MiniMed™ 780G [MM780G], CamAPS FX, Insulet Omnipod™ 5, Tandem Control™ IQ, Beta Bionics iLet™ and Diabeloop DBLG1 systems) in various countries each having different algorithmic approaches to managing glucose. Real-world data demonstrating the effectiveness of these systems comes from single centers, single countries, or multiple countries within a region. We hypothesized that the MM780G, an advanced hybrid closed-loop system with an algorithm that provides automated basal and correction insulin doses up to every 5 minutes and several glucose target (GT) and active insulin time (AIT) settings, could mitigate regional, cultural and dietary differences in glycemic outcomes in people with T1D and provide similar time in range (TIR, 70-180mg/dL), time above range (TAR 180mg/dL), time below range (TBR 70mg/dL) and glucose management indicator (GMI) and percentage of users reaching international consensus glycemic targets, across the globe. We also studied whether using the recommended optimal settings (ROS, 100mg/dL GT and 2hrs AIT ≥95% of the time) further narrows those differences. Methods: CareLink™ data of consenting MM780G users (any age, with ≥10 days of CGM use) that were uploaded since commercial availability in the following regions; Europe, Middle East and Africa (EMEA) (median 356 days of use), United States (US) (174.7 days), Asia-Pacific (APAC) (245 days), Latin America (LATAM) (213 days) and Canada (CAN) (261.9 days) were de-identified, aggregated and analyzed. The number of users in each region was >10,000 (>8,000 for CAN). Results: During overall settings use, mean %TIR and GMI were 72.1% and 6.9% (EMEA), 73.1% and 7.0% (US), 70.7% and 7.0% (APAC), and 74.0% and 6.8% (LATAM) and 72.6% and 7.0% (CAN). For all regions, mean %TAR was 23-27%, %TBR was ≤2.6% and 40-50% met consensus-recommended targets for all four metrics. In the 10-30% using the ROS, the %TIR and GMI were 77.6% and 6.8% in the EMEA, 78.0% and 6.8% in US, 76.3% and 6.8% in APAC, 78.1% and 6.7% in LATAM, and 78.3% and 6.7% in CAN. Mean %TAR was reduced to <21% and %TBR was ≤2.6% across regions. ROS use allowed 60-70% of users to meet targets. The trends in glycemic outcomes and rates meeting glycemic targets were observed for a majority of countries within the regions. Conclusion: The data show similar glycemic outcomes in real-world MM780G users across the world suggesting the algorithmic approach employed by the MM780G may be democratizing the management of T1D across cultures. The use of ROS increased the percentage reaching consensus-recommended targets by ∼20%. Presentation: Saturday, July 12, 2025
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".