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用于大规模数据集中快速估算胰岛素敏感性和β细胞反应性的自动化口服最小模型:一项验证研究

Automated Oral Minimal Models for Rapid Estimation of Insulin Sensitivity and Beta-Cell Responsivity in Large-Scale Data Sets: A Validation Study.

作者信息

Perazzolo Simone, Galderisi Alfonso, Carr Alice, Dayan Colin, Cobelli Claudio

机构信息

Nanomath LLC, Spokane, WA, USA.

University of Washington, Seattle, WA, USA.

出版信息

J Diabetes Sci Technol. 2025 Sep 3:19322968251365274. doi: 10.1177/19322968251365274.

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 et al (, 2008), accurately reproducing key parameters such as insulin sensitivity () and beta-cell responsivity () with high precision and substantial time savings. AOMM, with its user-friendly interface, facilitates broader application of minimal modeling in research and clinical studies.

摘要

口服最小模型(OMM)分析提供了葡萄糖耐量试验期间葡萄糖-胰岛素调节的独特测量方法。然而,其逐次手动测试的实施方式限制了大型研究中的可扩展性。我们引入了自动口服最小模型(AOMM),这是一种在保持分析准确性的同时简化并自动化整个OMM工作流程的工具,能够对大型数据集进行高效批量处理。基于SAAM II软件构建的AOMM,通过与[作者姓名]等人([文献年份],2008年)手动提取的结果进行验证,高精度地再现了诸如胰岛素敏感性()和β细胞反应性()等关键参数,且大大节省了时间。AOMM拥有用户友好的界面,有助于最小模型在研究和临床研究中更广泛地应用。

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本文引用的文献

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