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首页> 外文期刊>Journal of Diabetes Science and Technology >Factors Associated With Glycemic Control During Free-Living Overnight Closed-Loop Insulin Delivery in Children and Adults With Type 1 Diabetes
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Factors Associated With Glycemic Control During Free-Living Overnight Closed-Loop Insulin Delivery in Children and Adults With Type 1 Diabetes

机译:1型糖尿病儿童和成人的自由通宵隔夜胰岛素输送过程中与血糖控制相关的因素

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Unsupervised free-living overnight home use of closed-loop insulin delivery is feasible, safe, and effective in adolescents~( 1 )and adults~( 2 )with type 1 diabetes, but outcomes vary between individuals. Understanding factors influencing glucose outcomes may help to identify vulnerable populations, guide design of future studies, and lead to enhanced control algorithms. To explore associations between demographic characteristics, the use of closed-loop and glucose performance, we pooled data from 2 multicenter trials, 1 involving adolescents,~( 1 )and 1 involving adults~( 2 )with type 1 diabetes. Both studies adopted an open-label, cross-over, randomized controlled study design. Participants were randomly assigned to 4 (adults) or 3 (adolescents) weeks of sensor-augmented pump therapy with or without overnight closed-loop. An identical model-predictive-control algorithm was used in both studies.~( 3 )Participants were instructed to start the system at home after their evening meal and to discontinue it before breakfast the next morning. Detailed methods and results are reported elsewhere.~( 1 - 2 ) In the present work, Pearson’s correlation coefficients quantified the relationship between baseline demographic factors (age, BMI, HbA1c, total daily dose), participant-level utility characteristics (average duration of closed-loop application, average start time of closed-loop) and closed-loop outcomes between midnight and 08:00 (mean glucose, time in target between 70 and 145 mg/dl, time below 70 mg/dl) ( Table 1 ). Age and time below target were rank-normal transformed. Associations with gender were evaluated applying Spearman correlation. Multiple linear regression analysis quantified the amount of explained variability of closed-loop outcomes using demographic and utility characteristics. Table 1. Pearson’s Correlation Coefficients Between Closed-Loop Outcomes and Demographic and Utility Characteristics (N = 40). Age BMI HbA1c Total daily dose Duration of closed-loop application Time of closed-loop start Mean glucose ( P value) .17 (.294) .10 (.550) .52 (.001) ?.25 (.119) ?.20 (.209) .25 (.117) Time in target 70-145 mg/dl ( P value) ?.33 (.038) ?.24 (.129) ?.26 (.101) .27 (.097) .30 (.064) ?.30 (.064) Time below 70 mg/dl ( P value) .04 (.786) .14 (.386) ?.43 (.006) .06 (.702) ?.12 (.473) ?.25 (.127) Forty participants completed the studies, including 24 adults (age 43 ± 12 years [mean ± SD]; HbA1C 64.9 ± 8.9mmol/mol, 8.1 ± 0.8%; BMI 26.0 ± 3.5kg/m~(2); total daily insulin dose 0.5 ± 0.1U/kg/day) and 16 adolescents (age 15.6 ± 2.1 years; HbA1C 63.9 ± 9.4mmol/mol, 8.0 ± 0.9%; BMI 22.4 ± 3.7kg/m2; total daily insulin dose 0.8 ± 0.2U/kg/day). Data on 866 closed-loop nights were analyzed. HbA1c at baseline was associated with mean glucose during closed-loop nights ( r = .52, P = .001) and time with hypoglycemia ( r = –.43, P = .006), but not time in target ( r = –.26, P = .101). Early closed-loop start and longer closed-loop application tended to increase time in target ( P = .064). There was an age-associated reduction in time in target ( r = –.33, P = .038), perhaps reflecting the association between older age and shorter period of closed-loop use ( r = –.58, P < .001). Of the variance in mean glucose, 33% was explained by the regression model ( P = .028), with HbA1c as the only significant predictor ( P = .001). For time below target, the explained variance was 36% ( P = .017); earlier closed-loop start time ( P = .017) and HbA1c ( P = .008) were significant predictors. Only 20% of variance in time in target was explained by the regression model. The strength of the current work is that the data were collected during free-living unsupervised home closed-loop use. Weaknesses include that we did not capture at all or with low confidence other potentially influential factors such as socioeconomic and educational status, exercise patterns, and meal size and composition. In conclusion, in adolescents and adults with type 1 diabetes undergoing overnight closed-loop, baseline HbA1c is correlated with mean overnight glucose but not time in target range. Despite closed-loop, a lower HbA1c level remains a risk factor for nocturnal hypoglycemia. Improved time in target may be observed if overnight closed-loop is started earlier and applied for longer.
机译:在患有1型糖尿病的青少年〜(1)和成年人〜(2)中,无监督的封闭式胰岛素免费在家过夜通宵使用是可行,安全且有效的,但结局因人而异。了解影响血糖结果的因素可能有助于识别易感人群,指导未来研究的设计,并导致增强的控制算法。为了探讨人口统计学特征,闭环使用和血糖表现之间的关联,我们汇总了2项多中心试验的数据,其中1项涉及青少年,〜(1)和1项涉及成年人〜(2)1型糖尿病。两项研究均采用开放标签,交叉,随机对照研究设计。参与者被随机分为4周(成人)或3周(青少年),接受或不接受通宵闭环的传感器增强泵治疗。两项研究均使用了相同的模型预测控制算法。(3)要求参与者在晚餐后在家中启动系统,并在第二天早晨早餐前中止该系统。详细的方法和结果在其他地方报道。〜(1-2)在本研究中,Pearson的相关系数量化了基线人口统计学因素(年龄,BMI,HbA1c,每日总剂量),参与者水平的效用特征(平均持续时间)之间的关系。闭环应用,闭环的平均开始时间)和午夜至08:00之间的闭环结果(平均葡萄糖,目标时间在70和145 mg / dl之间,低于70 mg / dl的时间)(表1) 。低于目标的年龄和时间按等级进行正常变换。应用Spearman相关性评估与性别的关联。多元线性回归分析使用人口统计和效用特征量化了解释的闭环结果变异性的数量。表1.闭环结果与人口统计学和效用特征之间的Pearson相关系数(N = 40)。年龄BMI HbA1c日总剂量闭环应用的持续时间闭环开始的时间平均葡萄糖(P值).17(.294).10(.550).52(.001)?.25(.119)? .20(.209).25(.117)达到目标的时间70-145 mg / dl(P值).33(.038)..24(.129)?.26(.101).27(。 097).30(.064)?.30(.064)低于70 mg / dl(P值)的时间.04(.786).14(.386)?.43(.006).06(.702) ?.12(.473)?.25(.127)四十名参与者完成了研究,其中包括24名成人(年龄43±12岁[平均±SD]; HbA1C 64.9±8.9mmol / mol,8.1±0.8%; BMI 26.0 ±3.5kg / m〜(2);每日总胰岛素剂量0.5±0.1U / kg / day)和16个青少年(年龄15.6±2.1岁; HbA1C 63.9±9.4mmol / mol,8.0±0.9%; BMI 22.4±3.7) kg / m2;每日总胰岛素剂量为0.8±0.2U / kg /天)。分析了866个闭环夜的数据。基线时的HbA1c与闭环夜间的平均血糖(r = .52,P = .001)和低血糖时间(r = –.43,P = .006)相关,而与目标时间无关(r = – .26,P = .101)。早期的闭环启动和更长的闭环应用趋向于增加目标时间(P = .064)。与目标相关的时间减少(r = –.33,P = .038),这可能反映了老年人与闭环使用时间较短之间的关联(r = –.58,P <.001 )。在平均血糖方差中,回归模型解释了33%(P = .028),而HbA1c是唯一的重要预测因子(P = .001)。对于低于目标的时间,解释的方差为36%(P = .017);较早的闭环起始时间(P = .017)和HbA1c(P = .008)是重要的预测指标。回归模型仅解释了目标时间变化的20%。当前工作的优势在于,数据是在自由生活的无监督家庭闭环使用期间收集的。弱点包括我们根本没有或没有把握地捕捉到其他潜在影响因素,例如社会经济和教育状况,锻炼方式以及进餐量和组成。总之,在经历过夜闭环治疗的1型糖尿病的青少年和成年人中,基线HbA1c与平均过夜葡萄糖相关,但与目标范围内的时间无关。尽管是闭环的,但是较低的HbA1c水平仍然是夜间低血糖的危险因素。如果更早开始夜间闭环并应用更长的时间,则可以观察到目标时间的缩短。

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