Ladyzyński Piotr, Wójcicki Jan M, Bak Marianna, Sabalińska Stanisława, Kawiak Jerzy, Foltyński Piotr, Krzymień Janusz, Karnafel Waldemar
Department of Biomeasurements and Biocontrol, Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, 4 Trojdena Str., Warsaw, 02-109, Poland.
Ann Biomed Eng. 2008 Jul;36(7):1188-202. doi: 10.1007/s10439-008-9508-x. Epub 2008 May 1.
Glycated hemoglobin A1c (HbA1c) concentration in blood is an index of the glycemic control widely used in diabetology. The aim of the work was to validate two mathematical models of HbA1c formation (assuming irreversible or reversible glycation, respectively) and select a model, which was able to predict changes of HbA1c concentration in response to varying glycemia courses with higher accuracy. The experimental procedure applied consisted of an original combination of: in vivo continuous glucose concentration monitoring, long-term in vitro culturing of the human erythrocytes and mathematical modeling of HbA1c formation in vivo and in vitro with HbA1c values scaled according to the most specific analytical methods. Sixteen experiments were conducted in vitro using blood samples collected from healthy volunteer and stable type 1 diabetic patients whose glycemia was estimated beforehand based on long-term monitoring. The mean absolute difference of the measured and predicted HbA1c concentrations for the in vitro experiments were equal to 0.64 +/- 0.29% and 1.42 +/- 0.16% (p = 0.0007) for irreversible and for reversible model, respectively, meaning that the irreversible model was able to predict the glycation kinetics with a higher accuracy. This model was also more sensitive to a deviation of the erythrocytes life span.
血液中糖化血红蛋白A1c（HbA1c）浓度是糖尿病学中广泛使用的血糖控制指标。这项工作的目的是验证两种HbA1c形成的数学模型（分别假设糖化是不可逆的或可逆的），并选择一个能够更准确地预测HbA1c浓度随血糖变化过程而变化的模型。所应用的实验程序包括以下内容的原始组合：体内连续葡萄糖浓度监测、人红细胞的长期体外培养以及体内和体外HbA1c形成的数学建模，其中HbA1c值根据最特异的分析方法进行了缩放。使用从健康志愿者和1型稳定糖尿病患者采集的血样进行了16次体外实验，这些患者的血糖根据长期监测预先进行了评估。体外实验中测量和预测的HbA1c浓度的平均绝对差值，不可逆模型为0.64±0.29%，可逆模型为1.42±0.16%（p = 0.0007），这意味着不可逆模型能够更准确地预测糖化动力学。该模型对红细胞寿命的偏差也更敏感。
