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Postprandial glucose‐management strategies in type 1 diabetes: Current approaches and prospects with precision medicine and artificial intelligence

餐后 2型糖尿病 医学 胰岛素 糖尿病 餐食 内科学 内分泌学 重症监护医学 生物信息学 生物
作者
Adnan Jafar,Melissa‐Rosina Pasqua
出处
期刊:Diabetes, Obesity and Metabolism [Wiley]
卷期号:26 (5): 1555-1566 被引量:5
标识
DOI:10.1111/dom.15463
摘要

Abstract Postprandial glucose control can be challenging for individuals with type 1 diabetes, and this can be attributed to many factors, including suboptimal therapy parameters (carbohydrate ratios, correction factors, basal doses) because of physiological changes, meal macronutrients and engagement in postprandial physical activity. This narrative review aims to examine the current postprandial glucose‐management strategies tested in clinical trials, including adjusting therapy settings, bolusing for meal macronutrients, adjusting pre‐exercise and postexercise meal boluses for postprandial physical activity, and other therapeutic options, for individuals on open‐loop and closed‐loop therapies. Then we discuss their challenges and future avenues. Despite advancements in insulin delivery devices such as closed‐loop systems and decision‐support systems, many individuals with type 1 diabetes still struggle to manage their glucose levels. The main challenge is the lack of personalized recommendations, causing suboptimal postprandial glucose control. We suggest that postprandial glucose control can be improved by (i) providing personalized recommendations for meal macronutrients and postprandial activity; (ii) including behavioural recommendations; (iii) using other personalized therapeutic approaches (e.g. glucagon‐like peptide‐1 receptor agonists, sodium‐glucose co‐transporter inhibitors, amylin analogues, inhaled insulin) in addition to insulin therapy; and (iv) integrating an interpretability report to explain to individuals about changes in treatment therapy and behavioural recommendations. In addition, we suggest a future avenue to implement precision recommendations for individuals with type 1 diabetes utilizing the potential of deep reinforcement learning and foundation models (such as GPT and BERT), employing different modalities of data including diabetes‐related and external background factors (i.e. behavioural, environmental, biological and abnormal events).
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