Automated oral minimal models for rapid estimation of insulin sensitivity and beta-cell responsivity in large-scale data sets: a validation study

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

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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
Last Modified:	15 Sep 2025 14:30
URI:	https://orca.cardiff.ac.uk/id/eprint/181097

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