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使用持续葡萄糖监测来诊断2型1期糖尿病。

The Use of Continuous Glucose Monitoring to Diagnose Stage 2 Type 1 Diabetes.

作者信息

Mader Julia K, Wong Jenise C, Freckmann Guido, Garcia-Tirado Jose, Hirsch Irl B, Johnson Suzanne Bennett, Kerr David, Kim Sun H, Lal Rayhan, Montaser Eslam, O'Donnell Holly, Pleus Stefan, Shah Viral N, Ayers Alessandra T, Ho Cindy N, Biester Torben, Dovc Klemen, Farrokhi Farnoosh, Fleming Alexander, Gillard Pieter, Heinemann Lutz, López-Díez Raquel, Maahs David M, Mathieu Chantal, Quandt Zoe, Rami-Merhar Birgit, Wolf Wendy, Klonoff David C

机构信息

Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.

Division of Endocrinology, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA.

出版信息

J Diabetes Sci Technol. 2025 May 30:19322968251333441. doi: 10.1177/19322968251333441.

Abstract

This consensus report evaluates the potential role of continuous glucose monitoring (CGM) in screening for stage 2 type 1 diabetes (T1D). CGM offers a minimally invasive alternative to venous blood testing for detecting dysglycemia, facilitating early identification of at-risk individuals for confirmatory blood testing. A panel of experts reviewed current evidence and addressed key questions regarding CGM's diagnostic accuracy and screening protocols. They concluded that while CGM cannot yet replace blood-based diagnostics, it holds promise as a screening tool that could lead to earlier, more effective intervention. Metrics such as time above range >140 mg/dL could indicate progression risk, and artificial intelligence (AI)-based modeling may enhance predictive capabilities. Further research is needed to establish CGM-based diagnostic criteria and refine screening strategies to improve T1D detection and intervention.

摘要

本共识报告评估了持续葡萄糖监测(CGM)在2期1型糖尿病(T1D)筛查中的潜在作用。对于检测血糖异常,CGM提供了一种微创替代静脉血检测的方法,有助于早期识别有风险的个体以便进行确诊性血液检测。一个专家小组审查了当前证据,并讨论了有关CGM诊断准确性和筛查方案的关键问题。他们得出结论,虽然CGM目前尚不能取代基于血液的诊断方法,但作为一种筛查工具,它有望实现更早、更有效的干预。血糖高于140 mg/dL等指标可能表明进展风险,基于人工智能(AI)的模型可能会增强预测能力。需要进一步研究以建立基于CGM的诊断标准并完善筛查策略,从而改善T1D的检测和干预。

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