Moscoso-Vasquez Marcela, Colmegna Patricio, Barnett Charlotte, Fuller Morgan, Koravi Chaitanya L K, Brown Sue A, DeBoer Mark D, Breton Marc D
Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA.
Division of Endocrinology and Metabolism, Department of Medicine, University of Virginia, Charlottesville, Virginia, USA.
Diabetes Technol Ther. 2025 Feb;27(2):93-100. doi: 10.1089/dia.2024.0315. Epub 2024 Nov 6.
Automated insulin delivery (AID) is widely available to people with type 1 diabetes (T1D), providing superior glycemic control versus traditional methods. The next generation of AID devices focus on minimizing user/device interactions, especially around meals ("full closed loop," [FCL]). Our goal was to assess the postprandial glycemic impact of the bolus priming system (BPS), an algorithm delivering fixed insulin doses based on the likelihood of a meal having occurred, in conjunction with UVA's latest AID. Eleven adults with T1D participated in a supervised randomized-crossover trial assessing glycemic control during two 24-h sessions with identical meals and activity-with and without BPS. On the day in-between study sessions, participants underwent food and activity challenges to test BPS safety and robustness. Continuous glucose monitor (CGM) outcomes and total insulin doses were assessed overall and following meals with potential for BPS to dose additional insulin (CGM >90 mg/dL for 1 h prior). Daytime CGM outcomes were similar with and without BPS: time-in-range (TIR) 70-180 mg/dL 70.6% [62.2-76.5] versus 65.7% [58.6%-80.6%]; time-below-range <70 mg/dL 0% [0-2.1] versus 0% [0-1.3]; respectively. Insulin delivery during 3 h postprandial was indistinguishable 33.5 U [26.4-47.0] versus 35.7 U [28.7-44.9]. Among 43 out of 66 meals with potential to trigger BPS (24/19 BPS/no-BPS), postprandial incremental area-under-the-curve (iAUC) was lower for BPS versus no-BPS (2530 ± 1934 versus 3228 ± 2029, = 0.047), but CGM outcomes were inconclusive: 4-h-TIR 51.2% [19.8-83.3] versus 40.2% [20.8-56.3] ( = 0.24). There were no severe adverse events. While there was no difference in TIR, when BPS was active an improved postprandial AUC in FCL was obtained via earlier insulin injection.
自动胰岛素给药(AID)已广泛应用于1型糖尿病(T1D)患者,与传统方法相比,其血糖控制效果更佳。下一代AID设备致力于减少用户与设备之间的交互,尤其是在进餐期间(“全闭环”,[FCL])。我们的目标是评估推注预充系统(BPS)的餐后血糖影响,该算法根据进餐可能性提供固定剂量胰岛素,并结合弗吉尼亚大学最新的AID。11名患有T1D的成年人参与了一项有监督的随机交叉试验,评估在两个24小时时段内,相同饮食和活动条件下(有和没有BPS)的血糖控制情况。在两个研究时段之间的那天,参与者接受了食物和活动挑战,以测试BPS的安全性和稳定性。总体评估连续血糖监测(CGM)结果和总胰岛素剂量,并在餐后评估BPS额外给药的可能性(之前1小时CGM>90 mg/dL)。有BPS和没有BPS时的日间CGM结果相似:血糖在70-180 mg/dL范围内的时间(TIR)分别为70.6% [62.2-76.5]和65.7% [58.6%-80.6%];血糖低于范围<70 mg/dL的时间分别为0% [0-2.1]和0% [0-1.3]。餐后3小时的胰岛素给药量无差异,分别为33.5 U [26.4-47.0]和35.7 U [28.7-44.9]。在66餐中有43餐有可能触发BPS(24/19 BPS/无BPS),BPS组的餐后增量曲线下面积(iAUC)低于无BPS组(2530±1934对3228±2029,P = 0.047),但CGM结果尚无定论:4小时TIR分别为51.2% [19.8-83.3]和40.2% [20.8-56.3](P = 0.24)。没有严重不良事件。虽然TIR没有差异,但当BPS启动时,通过更早注射胰岛素可改善FCL中的餐后AUC。