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平均血糖和生物学变异对糖化血红蛋白(HbA1c)水平的影响大于血糖不稳定性:糖尿病控制与并发症试验数据分析

Mean blood glucose and biological variation have greater influence on HbA1c levels than glucose instability: an analysis of data from the Diabetes Control and Complications Trial.

作者信息

McCarter Robert J, Hempe James M, Chalew Stuart A

机构信息

Biostatistics and Informatics Unit, Children's Research Institute of Children's National Medical Center, Washington, DC, USA.

出版信息

Diabetes Care. 2006 Feb;29(2):352-5. doi: 10.2337/diacare.29.02.06.dc05-1594.

Abstract

OBJECTIVE

Mean blood glucose (MBG) over 2-3 months is a strong predictor of HbA(1c) (A1C) levels. Glucose instability, the variability of blood glucose levels comprising the MBG, and biological variation in A1C (BV) have also been suggested as predictors of A1C independent of MBG. To assess the relative importance of MBG, BV, and glucose instability on A1C, we analyzed patient data from the Diabetes Control and Complications Trial (DCCT).

RESEARCH DESIGN AND METHODS

A glucose profile set and sample for A1C were collected quarterly over the course of the DCCT from each participant (n = 1,441). The glucose profile set consisted of seven samples, one each drawn before and 90 min after breakfast, lunch, and dinner and one before bedtime. MBG and glucose instability (SD of blood glucose [SDBG]) were calculated as the arithmetic mean and SD of glucose profile set samples for each visit, respectively. A statistical model was developed to predict A1C from MBG, SDBG, and BV, adjusted for diabetes duration, sex, treatment group, stratum, and race.

RESULTS

Data from 32,977 visits were available. The overall model was highly statistically significant (log likelihood = -41,818.75, likelihood ratio chi2[7] = 7,218.71, P > chi2 = 0.0000). MBG and BV had large influences on A1C based on their standardized coefficients. SDBG had only 1/14 of the impact of MBG and 1/10 of the impact of BV.

CONCLUSIONS

MBG and BV have a large influence on A1C, whereas SDBG is relatively unimportant. Consideration of BV as well as MBG in the interpretation of A1C may enhance our ability to monitor diabetes management and predict complications.

摘要

目的

2 - 3个月的平均血糖(MBG)是糖化血红蛋白(HbA₁c,A1C)水平的有力预测指标。血糖不稳定性,即构成MBG的血糖水平变异性,以及A1C的生物学变异(BV)也被认为是独立于MBG的A1C预测指标。为评估MBG、BV和血糖不稳定性对A1C的相对重要性,我们分析了糖尿病控制与并发症试验(DCCT)中的患者数据。

研究设计与方法

在DCCT过程中,每季度从每位参与者(n = 1441)收集一组血糖谱数据和一份A1C样本。血糖谱数据由七个样本组成,分别在早餐、午餐和晚餐前及餐后90分钟以及睡前各采集一个样本。每次就诊时,MBG和血糖不稳定性(血糖标准差[SDBG])分别计算为血糖谱数据样本的算术平均值和标准差。建立了一个统计模型,根据MBG、SDBG和BV预测A1C,并对糖尿病病程、性别、治疗组、分层和种族进行了校正。

结果

获得了32977次就诊的数据。总体模型具有高度统计学意义(对数似然=-41818.75,似然比chi²[7]=7218.71,P>chi²=0.0000)。基于标准化系数,MBG和BV对A1C有很大影响。SDBG的影响仅为MBG的1/14和BV的1/10。

结论

MBG和BV对A1C有很大影响,而SDBG相对不重要。在解释A1C时考虑BV以及MBG可能会增强我们监测糖尿病管理和预测并发症的能力。

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