Building a Better A1C Test for Diabetes Diagnosis, Care

The test of a person’s glycated hemoglobin (A1C), which reflects the average glucose concentration over the past 3 months, remains the best way to diagnose whether someone has diabetes or, after that, to gauge how well the person is managing their condition. An A1C of 6.5% is used to diagnose diabetes, and most treatment goals call for bringing A1C to 7% or less.

The trouble is, it’s well known that the A1C test is not perfect. Because it is an average, variations are acknowledged, and practitioners typically encourage those with diabetes to record their blood sugar daily to provide a more complete picture of the state of their disease.

But what if the A1C test could be more precise? That’s what researchers from Harvard Medical School are trying to do with a new mathematical model, presented in an article from Wednesday in Science Translational Medicine. As they note, variations in the current test mean that “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 (A1C) values may have true average glucose concentrations that differ by more than 60 mg/dL.”

Roy Malka, PhD; David M. Nathan, MD; and John M. Higgins, MD, created their model, based on individual patients, of how red cells behave and how hemoglobin gathers blood sugar. They discovered that the older the red blood cells, the more time hemoglobin has to become glycated. The authors found that by taking the age of the red blood cells into account, they were able to create a model that personalized estimates of average glucose and substantially reduced error rates from the current standards in the process.

According to the researchers, their model reduced error rates from 1 in 3 to 1 in 10. “Our personalized approach should improve medical care for diabetes using existing clinical measurements,” they wrote.

In addition, the researchers tailored the model for patients who use continuous glucose monitoring, so that they can estimate their A1C status.

Reference

Malka R, Nathan DM, Higgins JM. Mechanistic modeling of hemoglobin glycation and red blood cell kinetics enables personalized diabetes monitoring. Sci Transl Med. 2016;8(359):359. doi: 10.1126/scitranslmed.aaf9304.