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Automated Oral Minimal Models for Rapid Estimation of Insulin Sensitivity and Beta-Cell Responsivity in Large-Scale
Simone Perazzolo1,2, Alfonso Galderisi3, Alice Carr4
1Nanomath LLC, Spokane, WA, USA.
Journal of Diabetes Science and Technology
|September 3, 2025
Summary
We developed an Automated Oral Minimal Model (AOMM) to efficiently analyze glucose-insulin regulation data. This tool automates manual processes, enabling large-scale studies and saving significant time.
Area of Science:
- Metabolic Research
- Computational Biology
- Biomedical Engineering
Background:
- Oral Minimal Model (OMM) analysis provides key insights into glucose-insulin dynamics.
- Manual OMM implementation is time-consuming and not scalable for large research studies.
Purpose of the Study:
- To introduce the Automated Oral Minimal Model (AOMM) for streamlined and efficient OMM analysis.
- To enable batch processing of large datasets while maintaining analytical accuracy.
Main Methods:
- Development of the AOMM tool integrated with SAAM II software.
- Validation of AOMM against manually derived results from existing studies.
- Assessment of key metabolic parameters like insulin sensitivity (Si) and beta-cell responsivity (Φ).
Main Results:
- AOMM accurately reproduced OMM parameters (Si, Φ) compared to manual analysis.
- Significant time savings were achieved through automated batch processing.
- High precision was maintained in parameter estimation.
Conclusions:
- AOMM offers a scalable and efficient solution for OMM analysis.
- The user-friendly interface of AOMM promotes wider adoption in research and clinical settings.
- Automated minimal modeling enhances the study of glucose-insulin regulation.
