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糖化血红蛋白与自我监测平均血糖:1型糖尿病动态追踪糖化血红蛋白估算值算法的验证

Hemoglobin A1c and Self-Monitored Average Glucose: Validation of the Dynamical Tracking eA1c Algorithm in Type 1 Diabetes.

作者信息

Kovatchev Boris P, Breton Marc D

机构信息

Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.

Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA

出版信息

J Diabetes Sci Technol. 2015 Nov 9;10(2):330-5. doi: 10.1177/1932296815608870.

Abstract

BACKGROUND

Previously we have introduced the eA1c-a new approach to real-time tracking of average glycemia and estimation of HbA1c from infrequent self-monitoring (SMBG) data, which was developed and tested in type 2 diabetes. We now test eA1c in type 1 diabetes and assess its relationship to the hemoglobin glycation index (HGI)-an established predictor of complications and treatment effect.

METHODS

Reanalysis of previously published 12-month data from 120 patients with type 1 diabetes, age 39.15 (14.35) years, 51/69 males/females, baseline HbA1c = 7.99% (1.48), duration of diabetes 20.28 (12.92) years, number SMBG/day = 4.69 (1.84). Surrogate fasting BG and 7-point daily profiles were derived from these unstructured SMBG data and the previously reported eA1c method was applied without any changes. Following the literature, we calculated HGI = HbA1c - (0.009 × Fasting BG + 6.8).

RESULTS

The correlation of eA1c with reference HbA1c was r = .75, and its deviation from reference was MARD = 7.98%; 95% of all eA1c values fell within ±20% from reference. The HGI was well approximated by a linear combination of the eA1c calibration factors: HGI = 0.007552θ1 + 0.007645θ2 - 3.154 (P < .0001); 73% of low versus moderate-high HGIs were correctly classified by the same factors as well.

CONCLUSIONS

The eA1c procedure developed in type 2 diabetes to track in real-time changes in average glycemia and present the results in HbA1c-equivalent units has shown similar performance in type 1 diabetes. The eA1c calibration factors are highly predictive of the HGI, thereby explaining partially the biological variation causing discrepancies between HbA1c and its linear estimates from SMBG data.

摘要

背景

此前我们引入了eA1c——一种实时追踪平均血糖水平并根据不频繁的自我血糖监测(SMBG)数据估算糖化血红蛋白(HbA1c)的新方法,该方法已在2型糖尿病患者中研发并进行了测试。我们现在对1型糖尿病患者进行eA1c测试,并评估其与血红蛋白糖化指数(HGI)的关系,HGI是一种已确立的并发症和治疗效果预测指标。

方法

重新分析先前发表的120例1型糖尿病患者的12个月数据,患者年龄39.15(14.35)岁,男性51例/女性69例,基线糖化血红蛋白(HbA1c)=7.99%(1.48),糖尿病病程20.28(12.92)年,每日自我血糖监测次数=4.69(1.84)次。从这些非结构化的自我血糖监测数据中得出替代空腹血糖和7点每日血糖谱,并应用先前报道的eA1c方法,未作任何更改。按照文献,我们计算了HGI = HbA1c -(0.009×空腹血糖 + 6.8)。

结果

eA1c与参考HbA1c的相关性为r = 0.75,其与参考值的偏差为平均绝对相对偏差(MARD)=7.98%;所有eA1c值的95%落在参考值的±20%范围内。HGI可以通过eA1c校准因子的线性组合得到很好的近似:HGI = 0.007552θ1 + 0.007645θ2 - 3.154(P < 0.0001);同样的因子也能正确分类73%的低与中 - 高HGI。

结论

在2型糖尿病中研发的用于实时追踪平均血糖变化并以等效HbA1c单位呈现结果的eA1c程序,在1型糖尿病中表现出相似的性能。eA1c校准因子对HGI具有高度预测性,从而部分解释了导致HbA1c与其从自我血糖监测数据得出的线性估计值之间存在差异的生物学变异。

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