New approach reduces significant errors from about one in three to about one in 10.
John Higgins, M.D., associate professor of systems biology at Harvard Medical School in Boston, along with Dr. Roy Malka and David M. Nathan used an algorithm to analyze blood glucose levels through HbA1c testing. “This enabled the scientists to account for variations in the age of blood cells among different people,” Higgins told Diabetes in Control in a recent interview.
In more than 200 patients included in the study, Higgins said the new approach reduced significant errors from about one in three to about one in 10. These were errors large enough to affect treatment decisions, he added.
“We think our approach will enable many patients and their doctors to do a better job controlling blood sugar levels and reduce the long-term risks of heart attack, stroke, blindness, and kidney failure” associated with diabetes,” Higgins said.
For optimal medical care, people with diabetes and their doctors need to know exactly the patient’s recent average blood glucose. Higgins 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.
The amount of glycated hemoglobin (HbA1c) in diabetes 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 people with diabetes. The true average glucose concentration of a person without diabetes and a person with poorly controlled diabetes 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. The researchers 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 non-glycemic determinants of HbA1c, including mean red blood cell age. They found that between-patient variation in derived mean red blood cell age explains all glucose-independent variation in HbA1c. They then used the 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. The patient-specific method reduced this error rate to 1 in 10. Higgins believes this personalized approach should improve medical care for diabetes using existing clinical measurements.
- The true average glucose concentration of a person without diabetes and a person with poorly controlled diabetes may differ by less than 15 mg/dl
- For optimal medical care, diabetes patients and their healthcare providers need to know exactly the patient’s recent average blood glucose.
- This new method of determining a true HbA1c number will help to decide the best treatment.
To learn more, continue on to our exclusive interview with Dr. John Higgins, one of the investigators of the new A1c approach.