Perazzolo, Simone, Galderisi, Alfonso, Carr, Alice, Dayan, Colin  ORCID: https://orcid.org/0000-0002-6557-3462 and Cobelli, Claudio
2025.
Automated oral minimal models for rapid estimation of insulin sensitivity and beta-cell responsivity in large-scale data sets: a validation study.
Journal of Diabetes Science and Technology
10.1177/19322968251365274
|
Preview |
PDF
- Supplemental Material
Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (513kB) | Preview |
Preview |
PDF
- Accepted Post-Print Version
Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (424kB) | Preview |
Abstract
The Oral Minimal Model (OMM) analysis offers unique measures of glucose–insulin regulation during glucose challenges. However, its manual test-by-test implementation limits scalability in large studies. We introduce the Automated Oral Minimal Model (AOMM), a tool that streamlines and automates the entire OMM workflow while preserving analytical fidelity, enabling efficient batch processing of large datasets. Built on SAAM II software, AOMM was validated against manually extracted results from Sunehag et al (Obesity (Silver Spring), 2008), accurately reproducing key parameters such as insulin sensitivity (Si) and beta-cell responsivity (Φ) with high precision and substantial time savings. AOMM, with its user-friendly interface, facilitates broader application of minimal modeling in research and clinical studies.
| Item Type: | Article |
|---|---|
| Date Type: | Published Online |
| Status: | In Press |
| Schools: | Schools > Medicine |
| Publisher: | SAGE Publications (UK and US) |
| ISSN: | 1932-2968 |
| Date of First Compliant Deposit: | 23 September 2025 |
| Date of Acceptance: | 22 July 2025 |
| Last Modified: | 24 Sep 2025 08:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/181097 |
Actions (repository staff only)
 |
Edit Item |
Altmetric
Altmetric