{"title":"A Kinetic Model-Based Approach for Estimating Hemoglobin A1c Based on Average Glucose","authors":"J. Tasic, M. Takács, L. Kovács","doi":"10.1109/SACI58269.2023.10158607","DOIUrl":null,"url":null,"abstract":"In this paper, we estimate hemoglobin A1c based on measured average glucose by applying a kinetic mathematical model. We use a training dataset of 226 patients with type 1 diabetes to analyze their daily continuous glucose monitoring profiles and hemoglobin A1c values measured every 13 weeks. We review the irreversible and reversible mathematical models that describe the kinetics of hemoglobin A1c formation. To estimate the mean age of red blood cells, we used a method based on the gamma distribution. Estimation of hemoglobin A1c was performed by using the proposed rate equations for the chemical reactions between glucose levels and changes in the concentration of hemoglobin A0, aldimine intermediate and glycosylated hemoglobin. We compare measured and estimated hemoglobin A1c values to cA1culate their standard deviation. The application of the kinetic mathematical model leads to a small standard deviation between the estimated and measured hemoglobin A1c values.","PeriodicalId":339156,"journal":{"name":"2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SACI58269.2023.10158607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we estimate hemoglobin A1c based on measured average glucose by applying a kinetic mathematical model. We use a training dataset of 226 patients with type 1 diabetes to analyze their daily continuous glucose monitoring profiles and hemoglobin A1c values measured every 13 weeks. We review the irreversible and reversible mathematical models that describe the kinetics of hemoglobin A1c formation. To estimate the mean age of red blood cells, we used a method based on the gamma distribution. Estimation of hemoglobin A1c was performed by using the proposed rate equations for the chemical reactions between glucose levels and changes in the concentration of hemoglobin A0, aldimine intermediate and glycosylated hemoglobin. We compare measured and estimated hemoglobin A1c values to cA1culate their standard deviation. The application of the kinetic mathematical model leads to a small standard deviation between the estimated and measured hemoglobin A1c values.