1型糖尿病数字孪生治疗中血糖调节模型的灵敏度驱动个性化

中文摘要 AI 辅助

数字孪生在糖尿病研究中应用日益广泛，但再现个体血糖动态需要准确识别血糖调节模型参数。传统灵敏度分析可识别有影响的参数，但基于有限条件的排序可能遗漏在特定扰动或特定个体中重要的参数。因此，我们考察了动态输入输出条件下参数影响的幅度和时序，并评估常见排序是否在参与者间成立。我们使用1型糖尿病与运动倡议数据集中192名接受自动胰岛素输送治疗的参与者的数据，分析Hovorka血糖调节模型。我们将Sobol灵敏度分析扩展到时间序列，并在四种条件下对参数影响进行排序：全天血糖曲线、孤立膳食扰动、胰岛素推注和餐后反应。我们将条件特定结果合并为全局排序，并用于选择参与者特定识别的参数。与群体参数相比，限制在灵敏度衍生子集的识别将60分钟血糖预测的均方根误差降低了60%，降至约31 mg/dL。这些发现表明，全局排序能够捕捉跨个体和动态条件的参数影响。通过缩小需要识别的参数范围，该方法降低了计算成本，并可能加速个性化糖尿病数字孪生的开发。

英文摘要

Digital twins are increasingly used in diabetes research, but reproducing individual glucose dynamics requires accurate identification of glucoregulatory model parameters. Traditional sensitivity analysis can identify influential parameters, yet a ranking based on limited conditions may miss parameters that matter during specific disturbances or for particular individuals. We therefore examine both the magnitude and timing of parameter influence across dynamic input-output conditions and assess whether a common ranking holds across participants. We analyze the Hovorka glucoregulatory model using data from 192 participants receiving automated insulin delivery therapy in the Type 1 Diabetes and Exercise Initiative dataset. We extend Sobol sensitivity analysis to time series and rank parameter influence under four conditions: full-day profiles, isolated meal disturbances, insulin bolus injections, and postprandial responses. We combine the condition-specific results into a global ranking and use it to select parameters for participant-specific identification. Compared with population parameters, identification restricted to the sensitivity-derived subset reduces the root mean square error of 60-minute glucose predictions by 60%, to approximately 31 mg/dL. These findings suggest that a global ranking can capture parameter influence across individuals and dynamic conditions. By narrowing the parameters requiring identification, this approach reduces computational cost and could accelerate the development of personalized diabetes digital twins.

发表机构

• Inselspital, Bern University Hospital and University of Bern（伯尔尼大学医院因塞尔斯皮塔尔）
• Diabetes Center Berne（伯尔尼糖尿病中心）
• Graduate School for Cellular and Biomedical Sciences, University of Bern（伯尔尼大学细胞与生物医学研究生院）

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