利用连续血糖监测和机器学习估算 2 型糖尿病患者或高危人群的早餐特征。

IF 4.1 Q2 ENDOCRINOLOGY & METABOLISM
Ryan Pai, Souptik Barua, Bo Sung Kim, Maya McDonald, Raven A Wierzchowska-McNew, Amruta Pai, Nicolaas E P Deutz, David Kerr, Ashutosh Sabharwal
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引用次数: 0

摘要

背景:连续血糖监测(CGM)系统可对餐后血糖反应(PPGR)进行详细评估,为了解食物选择对血糖异常的影响提供了新的视角。然而,目前使用 CGM 分析 PPGR 的方法需要手动记录膳食,这限制了 CGM 驱动的应用(如个性化营养和居家糖尿病风险评估)的可扩展性:我们提出了一个机器学习(ML)框架,用于从 CGM 资料中自动识别和描述与早餐有关的 PPGR,对象是有糖尿病风险或正在接受非胰岛素治疗的 2 型糖尿病(T2D)成人:我们的 PPGR 估算框架采用随机森林 ML 算法,该算法是在 15 名佩戴 CGM 长达四周的非糖尿病成人身上训练出来的。我们在参与者的 CGM 数据中保留了一个子集,并在 36 名有 T2D 风险或未经胰岛素治疗的 T2D 患者的外部验证数据集上评估了该算法的性能:我们的算法对早餐PPGR的估计在曲线下增量面积和血糖上升方面与注释的PPGR没有统计学意义上的显著差异(两个数据集的P > .05),而在非糖尿病数据集(P = .005)中发现了早餐前血糖的微小差异,但在验证的T2D数据集(P = .18)中没有发现这种差异:我们设计了一个 ML 框架,用于从 CGM 数据中自动估计非糖尿病患者、高危患者或 T2D 患者的进餐时间。这为分析餐后血糖提供了一种更具扩展性的方法,提高了基于 CGM 的精准营养和糖尿病风险评估应用的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes.

Background: Continuous glucose monitoring (CGM) systems allow detailed assessment of postprandial glucose responses (PPGR), offering new insights into food choices' impact on dysglycemia. However, current approaches to analyze PPGR using a CGM require manual meal logging, limiting the scalability of CGM-driven applications like personalized nutrition and at-home diabetes risk assessment.

Objective: We propose a machine learning (ML) framework to automatically identify and characterize breakfast-related PPGRs from CGM profiles in adults at risk of or living with noninsulin-treated type 2 diabetes (T2D).

Methods: Our PPGR estimation framework uses a random forest ML algorithm trained on 15 adults without diabetes who wore a CGM for up to four weeks. The algorithm performance was evaluated on a held-out subset of the participants' CGM data as well as on an external validation data set of 36 individuals at risk for or with noninsulin-treated T2D.

Results: Our algorithm's estimations of breakfast PPGRs displayed no statistically significant differences to annotated PPGRs, in terms of incremental area under the curve and glucose rise (P > .05 for both data sets), while a small difference in prebreakfast glucose was found in the nondiabetes data set (P = .005) but not in the validation T2D data set (P = .18).

Conclusions: We designed an ML framework to automatically estimate the timing of meal events from CGM data in individuals without diabetes and in individuals at risk or with T2D. This could provide a more scalable approach for analyzing postprandial glycemia, increasing the feasibility of CGM-based precision nutrition and diabetes risk assessment applications.

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来源期刊
Journal of Diabetes Science and Technology
Journal of Diabetes Science and Technology Medicine-Internal Medicine
CiteScore
7.50
自引率
12.00%
发文量
148
期刊介绍: The Journal of Diabetes Science and Technology (JDST) is a bi-monthly, peer-reviewed scientific journal published by the Diabetes Technology Society. JDST covers scientific and clinical aspects of diabetes technology including glucose monitoring, insulin and metabolic peptide delivery, the artificial pancreas, digital health, precision medicine, social media, cybersecurity, software for modeling, physiologic monitoring, technology for managing obesity, and diagnostic tests of glycation. The journal also covers the development and use of mobile applications and wireless communication, as well as bioengineered tools such as MEMS, new biomaterials, and nanotechnology to develop new sensors. Articles in JDST cover both basic research and clinical applications of technologies being developed to help people with diabetes.
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