![]() It can have implications for insurance or driver's licenses in certain places, and also there is the risk of overtreatment. Number one, a diagnosis of diabetes is serious. At the patient level, overdiagnosis is a false positive, and the risk of that is two-fold. Q: What are the risks from diagnosing someone with diabetes who doesn’t actually have the disease?Ī: Overdiagnosis can suggest a higher burden of diabetes than truly exists, so that's a problem at the epidemiological level. But some have argued that people with anemia might be underdiagnosed, so, yes, both over- and under-diagnosis are problems to be concerned about. Results also can differ based on the kind of assays that are used.Ī: Overdiagnosis is a bigger concern. This suggests that there might be many other factors involved in individual variance such as genetics. For them, the measurement becomes unreliable and you get artificially high levels of diabetes diagnosis.Īlso, in individuals without diabetes, just a third of the variance of A1C is explained by glucose levels, age, and body mass index. Some populations, for example the African American population in the U.S., the African population in their own countries, and Caribbean populations, all have high levels of sickle cell disease and thalassemia. ![]() Other conditions, like advanced liver or kidney disease, also can impact A1C measurements. Anything that affects red blood cell survival can affect the A1C levels, such as acute blood loss, mutations of various amino acid sequences, sickle cell anemia, thalassemia, or iron deficiency anemia. So that assumes a normal red blood cell span of 120 days. Q: What causes this variability in response to the A1C test?Ī: As I said earlier, A1C captures hemoglobin glycation over a 120-day period in the red blood cells. It is a considerable problem, especially in non-Caucasian populations in the United States and, also across the lower- and middle-income countries of the world, which have the largest burden of diabetes. Evidence of this problem has also come from African American and Hispanic populations who were part of the National Health and Nutrition Examination Survey (NHANES) and other surveys.Ī1C is a simple test, but when we use A1C alone for diagnosis, we are overestimating or underestimating the amount of diabetes in the population. She found that 19% of the study participants in Chennai, India, 27% in Delhi, India, and 11% in the United States had diabetes based on the A1C definition of around 6.5%, but not based on abnormal fasting glucose or the 2-hour glucose tests. My colleague Unjali Gujral looked at a cohort called the MASALA cohort (Mediators of Atherosclerosis in South Asians Living in America), which is Indian Americans living in California and Chicago, age 40 and over, and another cohort in India called the CARRS cohort (Centre for cArdiometabolic Risk Reduction in South-Asia Surveillance Study), also age 40 and over. Caucasians, from low- and middle-income countries. This difference has also been found in Asian American and Hispanic populations, and increasingly we're hearing reports of disparate results, compared to U.S. ![]() ![]() 25 –1.0 percentage point higher in African Americans compared to Caucasians in the United States, and this is not necessarily reflecting differences in glucose levels. However, in that same timeframe, a body of research has emerged showing that there is considerable variation in A1C results across populations.įor example, A1Cs may be. Over the past 10 to 15 years, A1C has become popular as a test for management of diabetes and, increasingly, as a test for diagnosis of diabetes. Put simply, it captures hemoglobin glycation over the past 120 days, which is the normal lifespan of the red blood cell. Can you describe the problem?Ī: We need to step back and understand what A1C is. Q: Research has found that the hemoglobin A1C test potentially misclassifies some people. In the final post of our Interpreting A1C blog series, he explains why diagnosing diabetes with the hemoglobin A1C test can sometimes be problematic and offers suggestions for using the test in patient care and research. Venkat Narayan, MD, MSc, MBA, is a professor of medicine & epidemiology at Emory University who has done substantial research exploring variability in type 2 diabetes across populations.
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