Outpatient Assessment of Determinants of Glucose Excursions in Adolescents with Type 1 Diabetes: Proof of Concept
Outpatient Assessment of Determinants of Glucose Excursions in Adolescents with Type 1 Diabetes: Proof of Concept
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DOI：
10.1089/dia.2012.0053
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发表时间：
2012-08-01
影响因子：
5.4
通讯作者：
McMurray, Robert G.
中科院分区：
文献类型：
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作者：
Maahs, David M.;Mayer-Davis, Elizabeth;McMurray, Robert G.
目的：有关于食物、体力活动（PA）和胰岛素剂量对血糖波动影响的对照住院研究，但此类门诊数据有限。我们在此报告了30例1型糖尿病（T1 D）青少年患者5天内血糖波动及其关键决定因素的门诊数据，作为一项原理验证性试点研究。（20例使用胰岛素泵，10例接受每日多次注射; 15 +/- 2岁;糖尿病病程，8 +/- 4年;血红蛋白A1 c，8.1 +/-1.0%）佩戴连续葡萄糖监测器（CGM）和加速度计5天。受试者继续其现有的胰岛素方案，并从胰岛素泵下载或胰岛素笔数字日志中获得带时间戳的胰岛素给药数据。时间戳的手机照片的食物前和消费后和食物日志被用来增加24小时饮食回忆的第1天和第3天。这些变量被纳入回归模型，以预测血糖波动在1-4小时postbreakth.Results：CGM数据在第1天和第3天获得的57个可能的60个受试者的天，平均125个每日CGM读数（出一个可能的144）。分别在第1天100%和93%的受试者以及第3天90%和100%的受试者中获得PA和饮食回忆数据。所有这些变量影响葡萄糖波动在清醒后1-4小时，和56的60个受试者天的建模analysis.Conclusions：门诊高分辨率的时间戳数据的主要输入的葡萄糖变异性在青少年T1 D是可行的，可以建模。未来的应用包括使用这些数据进行计算机模拟建模和监测闭环研究的门诊迭代，以及改善有关胰岛素给药的临床建议，以匹配饮食和PA行为。
Objective: Controlled inpatient studies on the effects of food, physical activity (PA), and insulin dosing on glucose excursions exist, but such outpatient data are limited. We report here outpatient data on glucose excursions and its key determinants over 5 days in 30 adolescents with type 1 diabetes (T1D) as a proof-of-principle pilot study.Subjects and Methods: Subjects (20 on insulin pumps, 10 receiving multiple daily injections; 15 +/- 2 years old; diabetes duration, 8 +/- 4 years; hemoglobin A1c, 8.1 +/- 1.0%) wore a continuous glucose monitor (CGM) and an accelerometer for 5 days. Subjects continued their existing insulin regimens, and time-stamped insulin dosing data were obtained from insulin pump downloads or insulin pen digital logs. Time-stamped cell phone photographs of food pre- and post-consumption and food logs were used to augment 24-h dietary recalls for Days 1 and 3. These variables were incorporated into regression models to predict glucose excursions at 1-4 h post-breakfast.Results: CGM data on both Days 1 and 3 were obtained in 57 of the possible 60 subject-days with an average of 125 daily CGM readings (out of a possible 144). PA and dietary recall data were obtained in 100% and 93% of subjects on Day 1 and 90% and 100% of subjects on Day 3, respectively. All of these variables influenced glucose excursions at 1-4 h after waking, and 56 of the 60 subject-days contributed to the modeling analysis.Conclusions: Outpatient high-resolution time-stamped data on the main inputs of glucose variability in adolescents with T1D are feasible and can be modeled. Future applications include using these data for in silico modeling and for monitoring outpatient iterations of closed-loop studies, as well as to improve clinical advice regarding insulin dosing to match diet and PA behaviors.