Research ArticleDiabetes

Mechanistic modeling of hemoglobin glycation and red blood cell kinetics enables personalized diabetes monitoring

Science Translational Medicine  05 Oct 2016:
Vol. 8, Issue 359, pp. 359ra130
DOI: 10.1126/scitranslmed.aaf9304

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A better estimate of blood glucose

For optimal medical care, diabetics and their doctors need to know exactly the patient’s recent average blood glucose. Malka and colleagues have developed a mathematical model to this end by integrating the mechanisms of hemoglobin glycation (an indication of blood glucose concentrations) and red blood cell kinetics. Combining the modeling with routine clinical measurements yielded personalized estimates of a patient’s average blood glucose that reduced diagnostic errors by more than 50% compared to the current method.

Abstract

The amount of glycated hemoglobin (HbA1c) in diabetic patients’ blood provides the best estimate of the average blood glucose concentration over the preceding 2 to 3 months. It is therefore essential for disease management and is the best predictor of disease complications. Nevertheless, substantial unexplained glucose-independent variation in HbA1c makes its reflection of average glucose inaccurate and limits the precision of medical care for diabetics. The true average glucose concentration of a nondiabetic and a poorly controlled diabetic may differ by less than 15 mg/dl, but patients with identical HbA1c values may have true average glucose concentrations that differ by more than 60 mg/dl. We combined a mechanistic mathematical model of hemoglobin glycation and red blood cell kinetics with large sets of within-patient glucose measurements to derive patient-specific estimates of nonglycemic determinants of HbA1c, including mean red blood cell age. We found that between-patient variation in derived mean red blood cell age explains all glucose-independent variation in HbA1c. We then used our model to personalize prospective estimates of average glucose and reduced errors by more than 50% in four independent groups of greater than 200 patients. The current standard of care provided average glucose estimates with errors >15 mg/dl for one in three patients. Our patient-specific method reduced this error rate to 1 in 10. Our personalized approach should improve medical care for diabetes using existing clinical measurements.

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