Gingras V, Rabasa-Lhoret R, Messier V, Ladouceur M, Legault L, Haidar A
Institut de recherches cliniques de Montréal, Montreal, Quebec, Canada; Department of nutrition, Université de Montréal, Montreal, Quebec, Canada.
Institut de recherches cliniques de Montréal, Montreal, Quebec, Canada; Department of nutrition, Université de Montréal, Montreal, Quebec, Canada; Montreal Diabetes Research Center (MDRC), Montreal, Quebec, Canada; Research Center of the Université de Montréal Hospital Center (CRCHUM), Montreal, Quebec, Canada; Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada.
Diabetes Metab. 2016 Feb;42(1):47-54. doi: 10.1016/j.diabet.2015.05.001. Epub 2015 Jun 10.
Carbohydrate-counting is a complex task for many patients with type 1 diabetes. This study examined whether an artificial pancreas, delivering insulin and glucagon based on glucose sensor readings, could alleviate the burden of carbohydrate-counting without degrading glucose control.
Twelve adults were recruited into a randomized, three-way, crossover trial (ClinicalTrials.gov identifier No. NCT01930097). Participants were admitted on three occasions from 7AM to 9PM and consumed a low-carbohydrate breakfast (women: 30g; men: 50g), a medium-carbohydrate dinner (women: 50g; men: 70g) and a high-carbohydrate lunch (women: 90g; men: 120g). At each visit, glucose levels were randomly regulated by: (1) conventional pump therapy; (2) an artificial pancreas (AP) accompanied by prandial boluses, matching the meal's carbohydrate content based on insulin-to-carbohydrate ratios (AP with carbohydrate-counting); or (3) an AP accompanied by prandial boluses based on qualitative categorization (regular or large) of meal size (AP without carbohydrate-counting).
The AP without carbohydrate-counting achieved similar incremental AUC values compared with carbohydrate-counting after the low- (P=0.54) and medium- (P=0.38) carbohydrate meals, but yielded higher post-meal excursions after the high-carbohydrate meal (P=0.004). The AP with and without carbohydrate-counting yielded similar mean glucose levels (8.2±2.1mmol/L vs. 8.4±1.7mmol/L; P=0.52), and both strategies resulted in lower mean glucose compared with conventional pump therapy (9.6±2.0mmol/L; P=0.02 and P=0.03, respectively).
The AP with qualitative categorization of meal size could alleviate the burden of carbohydrate-counting without compromising glucose control, although more categories of meal sizes are probably needed to effectively control higher-carbohydrate meals.
对许多1型糖尿病患者而言，计算碳水化合物含量是一项复杂的任务。本研究探讨了一种基于葡萄糖传感器读数输送胰岛素和胰高血糖素的人工胰腺是否能减轻计算碳水化合物含量的负担，同时又不影响血糖控制。
招募了12名成年人参加一项随机、三臂交叉试验（ClinicalTrials.gov标识符：NCT01930097）。参与者在上午7点至晚上9点期间分三次入院，分别食用低碳水化合物早餐（女性：30克；男性：50克）、中等碳水化合物晚餐（女性：50克；男性：70克）和高碳水化合物午餐（女性：90克；男性：120克）。每次就诊时，血糖水平通过以下方式随机调节：（1）传统泵治疗；（2）人工胰腺（AP）并给予餐时大剂量胰岛素，根据胰岛素与碳水化合物的比例匹配餐食的碳水化合物含量（计算碳水化合物含量的AP）；或（3）人工胰腺并根据餐量的定性分类（正常或大量）给予餐时大剂量胰岛素（不计算碳水化合物含量的AP）。
不计算碳水化合物含量的AP在低碳水化合物餐（P=0.54）和中等碳水化合物餐（P=0.38）后与计算碳水化合物含量相比，获得了相似的增量AUC值，但在高碳水化合物餐后餐后血糖波动更高（P=0.004）。计算和不计算碳水化合物含量的AP产生的平均血糖水平相似（8.2±2.1毫摩尔/升对8.4±1.7毫摩尔/升；P=0.52），与传统泵治疗相比，两种策略均导致平均血糖更低（9.6±2.0毫摩尔/升；分别为P=0.02和P=0.03）。
根据餐量进行定性分类的AP可以减轻计算碳水化合物含量的负担，同时不影响血糖控制，尽管可能需要更多的餐量类别来有效控制高碳水化合物餐。
