Bernhard Kulzer, Guido Freckmann, Ralph Ziegler, Oliver Schnell, Timor Glatzer, Lutz Heinemann
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引用次数: 0
摘要
夜间低血糖是接受胰岛素治疗的糖尿病患者常见的急性并发症。特别是,睡眠时无法控制血糖水平、运动或酒精等外部因素的影响以及激素的影响是主要原因。夜间低血糖对糖尿病患者的躯体、心理和社会都有一些负面影响,本文将对此进行总结。随着连续血糖监测仪(CGM)的出现,研究表明,使用传统血糖监测仪时,夜间低血糖事件的数量被明显低估。CGM 可借助警报、趋势箭头和评估程序减少夜间低血糖的发生次数。自动血糖调节系统(AID)与带有胰岛素泵和算法的 CGM 相结合,在夜间血糖调节和预防夜间低血糖方面具有独特的优势。然而,目前的技术还不能完全解决夜间低血糖的问题。使用预测模型对低血糖发出警告的 CGM 系统、能更好地识别低血糖模式的改进型 AID 系统,以及人工智能方法的不断整合,都是未来有望大大降低胰岛素治疗副作用风险的方法。
Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring.
Nocturnal hypoglycemia is a common acute complication of people with diabetes on insulin therapy. In particular, the inability to control glucose levels during sleep, the impact of external factors such as exercise, or alcohol and the influence of hormones are the main causes. Nocturnal hypoglycemia has several negative somatic, psychological, and social effects for people with diabetes, which are summarized in this article. With the advent of continuous glucose monitoring (CGM), it has been shown that the number of nocturnal hypoglycemic events was significantly underestimated when traditional blood glucose monitoring was used. The CGM can reduce the number of nocturnal hypoglycemia episodes with the help of alarms, trend arrows, and evaluation routines. In combination with CGM with an insulin pump and an algorithm, automatic glucose adjustment (AID) systems have their particular strength in nocturnal glucose regulation and the prevention of nocturnal hypoglycemia. Nevertheless, the problem of nocturnal hypoglycemia has not yet been solved completely with the technologies currently available. The CGM systems that use predictive models to warn of hypoglycemia, improved AID systems that recognize hypoglycemia patterns even better, and the increasing integration of artificial intelligence methods are promising approaches in the future to significantly minimize the risk of a side effect of insulin therapy that is burdensome for people with diabetes.
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.