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Mathematical models may help in improving diabetes diagnosis and monitoring
Thursday August 30, 2018

Diabetes mellitus is a growing global health burden affecting about 400 million people worldwide. A person’s glycated hemoglobin reflects the average concentration of glucose in the blood over the past 2 to 3 months and is the gold standard measure for estimating the risk for diabetes-related complications in patients with disease. Remarkably there exists a transient (reversible) form known as the glycated hemoglobin labile fraction, which also provides information about the average concentration of glucose in the blood plasma at a significantly shorter time scale. 

Molab researchers have put forward a biomathematical model to quantify the kinetics of two patient-specific glycemic biomarkers to track the emergence and evolution of diabetes mellitus: glycated hemoglobin and its labile fraction. The method incorporates erythrocyte age distribution and makes use of a large cohort of clinical data from blood sample analysis. Using the labile hemoglobine readings, that are available whenever the glycated hemoglobin is determined and mathematical models it may be possible to obtain information on other 


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