Acentral principle in science and medicine is that the more independent pieces of evidence there are that agree, the more convincing it is that a hypothesis—or diagnosis—is valid. The opposite is also true: discordant information leads to uncertainty. Unfortunately, it is not uncommon for clinicians caring for people with diabetes to encounter individuals in whom HbA1c and blood glucose simply do not match. Sometimes, there is an obvious explanation such as hemolytic anemia. But when it occurs in people with reliable blood glucose records and ostensibly normal peripheral blood and reticulocyte counts, without evidence of hemoglobinopathy, hemolytic disorder, blood loss/transfusion, or nutritional deficiency such as iron, folate, or vitamin B12, we are left with the questions of how the discordant information should be treated and what it means for patient care. Part of the challenge is that even the best characterization of the association between HbA1c and blood glucose shows an imperfect relationship in populations. For example, at an HbA1c of 6.0%, the mean blood glucose has a 95% CI ranging from 100 to 152 mg/dL. This overlaps with the 95% CI for the mean blood glucose at an HbA1c of 7.0%, which is 123–185 mg/dL (1). Such wide variation reinforces the notion that HbA1c and blood glucose are not exactly equivalent. Moreover, it raises the question of whether a binary cut point for HbA1c in the diagnosis of diabetes, such as 6.5% (2), is an adequate representation of blood glucose and suggests that reliance only on HbA1c could miss persons with diabetes and falsely diagnose those without (3,4). But, if we obtain both glucose measurements and HbA1c, we are left with what to do with discordant information. Three explanations are commonly advanced to explain the spread in the …
When the Blood Glucose and the HbA1c Don’t Match: Turning Uncertainty Into Opportunity
Published 2012 in Diabetes Care
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- Publication year
2012
- Venue
Diabetes Care
- Publication date
2012-11-14
- Fields of study
Medicine
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- Source metadata
Semantic Scholar, PubMed
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