Estimation of the hemoglobin glycation rate constant

Abstract

In a previous study, a method of obtaining mean erythrocyte age ($$M_{RBC}$$ M RBC ) from HbA1c and average plasma glucose (AG) was proposed. However, the true value of the hemoglobin glycation constant ($$k_g$$ k g dL/mg/day), required for this model has yet to be well characterized. Another study also proposed a method of deriving $$M_{RBC}$$ M RBC from erythrocyte creatine (EC). Utilizing these formulae, this study aimed to determine a more accurate estimate of $$k_g$$ k g . One hundred and seven subjects including 31 patients with hemolytic anemia and 76 subjects without anemia were included in this study. EC and HbA1c data were analyzed, and $$M_{RBC}$$ M RBC using HbA1c, AG and the newly-derived constant, $$k_g$$ k g were compared to $$M_{RBC}$$ M RBC using traditional $$^{51}\hbox {Cr}$$ 51 Cr in three patients whose data were taken from previous case studies. A value of $$7.0\times 10^{-6}$$ 7.0 × 10 - 6 dL/mg/day was determined for $$k_g$$ k g . $$M_{RBC}$$ M RBC using HbA1c, AG and $$k_g$$ k g were found to no be significantly different (paired t-test, $$p=0.45$$ p = 0.45 ) to $$M_{RBC}$$ M RBC using traditional $$^{51}\hbox {Cr}$$ 51 Cr . $$k_g$$ k g enables the estimation of $$M_{RBC}$$ M RBC from HbA1c and AG.

Estimation of mean erythrocyte age using HbA1c or HbA1c/glycated albumin for evaluation of anemia severity

“…A subsequent analysis determined that k g in Equation (4) was 7.0 × 10 −6 . 21 Therefore, the following equation was derived from Equation (4): This derived equation is almost the same as our proposed equation, M RBC = 1.45 × iA1c. Because HbA1c is affected by glycemic control in diabetic patients, M RBC cannot be estimated from HbA1c in diabetic patients.…”

“…In addition, an MBG level of 100 mg/dL can be used in non‐diabetic individuals. A subsequent analysis determined that k g in Equation () was 7.0 × 10 −6 21 . Therefore, the following equation was derived from Equation ():…”

Integrated modeling of labile and glycated hemoglobin with glucose for enhanced diabetes detection and short-term monitoring

“…Over the past few years, the development of quantitative methods to forecast glucose metabolism levels has attracted considerable attention in biomedical research. 3 , 21 , 22 , 30 , 31 , 32 , 33 , 34 The advent of wearable monitoring technologies has further stimulated the advancement of increasingly complex mathematical models and efficient data processing algorithms capable of handling an influx of real-time datasets. 12 , 14 , 35 While these developments provide significant benefits for following distinct patient groups in clinical trials and validating emerging hypotheses and mechanisms, their wide accessibility remains a challenge.…”

AGEomics Biomarkers and Machine Learning—Realizing the Potential of Protein Glycation in Clinical Diagnostics

“…Thus, it may be beneficial to use protein-based biomarkers where major proteins in the clinical setting such as serum albumin, hemoglobin and skin collagen have approximate half-lives of ca. 20 days, 42 days and 15 years, respectively [2][3][4]. It is particularly advantageous where variation in levels of metabolites is captured over the lifespan of the protein by reaction of a metabolite with a protein to form a stable adduct.…”