The Potential for A1c to Underestimate Glycemic Control in Certain Populations

The hemoglobin A1c test remains a cornerstone of diabetes management, offering clinicians a retrospective view of average blood glucose over the preceding two to three months. Its convenience—no fasting required, single blood draw, standardized assays in many labs—has made it the default metric for diagnosing diabetes and assessing glycemic control in millions of patients. However, a growing body of evidence shows that A1c does not perform uniformly across all populations. In certain groups, the test may systematically underestimate or overestimate true glycemic control, leading to misclassification of diabetes risk, inadequate treatment adjustments, and ultimately worse outcomes. For clinicians and patients alike, understanding when and why A1c can be misleading is essential for delivering equitable, high-quality diabetes care.

Understanding the A1c Test and Its Limitations

The A1c assay measures the percentage of hemoglobin that is glycated — specifically, the fraction of hemoglobin A with glucose attached to the N-terminal valine of the beta-globin chain. Because red blood cells have a typical lifespan of approximately 120 days, the glycation level integrates glucose exposures over the preceding weeks. This makes A1c a reliable proxy for average glycemia when the test assumptions hold: normal hemoglobin structure, normal red blood cell survival, and no interfering conditions. When any of these assumptions break down, the relationship between A1c and true mean glucose can diverge significantly.

Biological Variables That Skew A1c

Multiple non-glycemic factors influence A1c readings independently of actual blood glucose levels. Red blood cell lifespan is the most critical variable: any condition that shortens red cell survival (e.g., hemolysis, recent blood loss, treatment with erythropoiesis-stimulating agents) will lower A1c because there is less time for glycation to accumulate. Conversely, conditions that prolong red cell survival or increase the proportion of younger cells can artificially elevate A1c. Hemoglobin variants—such as HbS, HbC, HbE, and HbF—can interfere with assay methods, particularly those relying on ion-exchange high-performance liquid chromatography or capillary electrophoresis, producing falsely low or high results depending on the variant and the specific assay.

Racial and Ethnic Disparities in A1c Interpretation

Perhaps the most clinically consequential limitation is the growing recognition of racial and ethnic differences in the A1c–glucose relationship. Studies have consistently shown that, for a given level of mean blood glucose, Black individuals tend to have higher A1c values than White individuals, while the relationship is more variable among Hispanic, Asian, and other groups. A landmark analysis from the Diabetes Control and Complications Trial (DCCT) and subsequent epidemiological studies found that these discrepancies are not explained by differences in glycemic control, body mass index, or socioeconomic factors. Instead, they appear to reflect intrinsic biological differences in hemoglobin glycation rates, red cell turnover, or glycation gap. If clinicians rely solely on A1c without adjusting for these differences, they risk undertreating diabetes in populations with biologically lower A1c readings relative to their true glucose load, or overtreating those with higher A1c values.

Populations at Risk for Underestimation (and Overestimation)

The populations most vulnerable to A1c inaccuracy fall into overlapping categories defined by genetic hemoglobin variants, hematologic conditions, and chronic diseases that alter erythrocyte biology.

Hemoglobin Variants: HbS, HbC, HbE, and Thalassemias

Hemoglobin variants are most prevalent in populations with ancestry from sub-Saharan Africa, the Mediterranean basin, the Middle East, and Southeast Asia. Sickle cell trait (HbAS) and sickle cell disease (HbSS) can shorten red blood cell lifespan, leading to a lower A1c for a given level of glycemia. In patients with sickle cell disease, A1c is often unreliable and should not be used as the sole measure of glycemic control. HbC trait and HbE trait—common in West Africa and Southeast Asia, respectively—can interfere with specific assays, sometimes producing spuriously low readings. Beta-thalassemia minor, characterized by reduced beta-globin synthesis and mild microcytic anemia, tends to lower A1c. Alpha-thalassemia trait similarly affects red cell indices and can skew results. Clinicians caring for patients from these ethnic groups should maintain a high index of suspicion when the A1c appears discordant with self-monitored blood glucose or clinical presentation.

Anemia: Iron Deficiency, B12/Folate Deficiency, and Anemia of Chronic Disease

Anemia is a global health problem affecting over 30% of the world’s population, with the highest burdens in South Asia, sub-Saharan Africa, and among women of reproductive age worldwide. The relationship between anemia and A1c is bidirectional and complex. Iron deficiency anemia (IDA) has been shown to increase A1c in some studies, possibly because iron deficiency alters red cell membrane properties or increases glycation susceptibility, while other studies have found no consistent effect. The direction of the bias may depend on the severity and chronicity of the anemia. Anemia of chronic disease (ACD), common in patients with diabetes, chronic kidney disease, inflammation, or malignancy, is associated with shortened red cell survival and reduced A1c relative to glucose levels. B12 and folate deficiency anemias, often seen in older adults, vegans, or patients with malabsorption, also produce macrocytic red cells with altered turnover. In any patient with unexplained discordance between A1c and glucose records, a complete blood count, iron studies, and vitamin B12 level are warranted.

Chronic Kidney Disease and Altered Erythropoiesis

Chronic kidney disease (CKD) affects red blood cell biology in multiple ways: reduced erythropoietin production leads to anemia with longer-lived transfused red cells or use of erythropoiesis-stimulating agents; these agents increase the proportion of young red cells with less glycation, lowering A1c. In addition, uremic toxins carbamylate hemoglobin, producing an A1c-like adduct that can interfere with some assays. The presence of CKD, particularly stage 4 or 5, is a strong indication that A1c alone is insufficient. Professional guidelines recommend using alternative measurements such as glycated albumin or fructosamine in patients with advanced CKD, especially those on dialysis.

Pregnancy and Rapid Red Cell Turnover

Pregnancy introduces a unique set of challenges. Physiologic hemodilution and increased erythropoiesis shorten the average red cell age, leading to a lower A1c for the same degree of glycemia compared to the non-pregnant state. For this reason, A1c is not recommended for the diagnosis of gestational diabetes mellitus, and oral glucose tolerance testing remains the standard. In women with pre-existing diabetes who become pregnant, fructosamine or CGM are often more reliable than A1c alone.

Clinical Consequences of Misestimated Glycemic Control

When A1c underestimates true glycemia, the result is clinical inertia: a healthcare provider looking at a seemingly good A1c may decide not to intensify therapy, while the patient is actually spending excessive time in hyperglycemia. Over the long term, this exposes patients to higher risks of microvascular complications (retinopathy, nephropathy, neuropathy) and potentially macrovascular events. Conversely, when A1c overestimates glucose, it can lead to overtreatment with agents that cause hypoglycemia, especially insulin and sulfonylureas, increasing the risk of severe hypoglycemic events. Health disparities in diabetes outcomes—particularly the higher rates of lower-extremity amputations and end-stage renal disease among Black and Hispanic adults in the United States—may be partially attributable to overreliance on a test that does not perform equally across groups. Recognizing these pitfalls is not about abandoning A1c, but about using it critically and complementing it with other data streams.

Alternative and Complementary Monitoring Strategies

A multi-modality approach to glycemic assessment is the most reliable path to individualized care. The following alternatives and adjuncts should be considered when A1c is suspected to be inaccurate or when clinical discordance exists.

Fructosamine and Glycated Albumin

Fructosamine measures glycated serum proteins, predominantly albumin. Because albumin has a half-life of approximately 14–20 days, fructosamine reflects average glycemia over the preceding 2–3 weeks — a shorter window than A1c. This makes it useful when A1c is unreliable due to hemoglobin variants, anemia, or recent changes in therapy. Glycated albumin is a more specific sub-fraction of fructosamine that has shown better correlation with mean glucose in patients with CKD and those on dialysis. A practical advantage: neither test depends on red blood cell biology. Drawbacks include sensitivity to changes in serum albumin levels (malnutrition, nephrotic syndrome, liver disease) and the lack of long-term outcome data equivalent to the DCCT/UKPDS trials that underpin A1c targets. Nevertheless, many guidelines now recommend glycated albumin as a complementary test in patients with hemoglobinopathies or advanced CKD. Learn more from the NIDDK overview of A1c and alternatives.

Continuous Glucose Monitoring (CGM)

Continuous glucose monitoring has transformed diabetes care by providing a continuous stream of glucose data that reveals patterns — postprandial excursions, nocturnal hypoglycemia, glycemic variability — that are invisible to A1c. Time-in-range (TIR), the percentage of readings between 70 and 180 mg/dL, has emerged as a validated outcome metric that correlates with A1c but offers richer clinical insight. For patients with conditions that skew A1c, CGM can serve as the primary tool for glycemic assessment. The American Diabetes Association Standards of Care now endorse TIR targets alongside A1c. CGM is particularly valuable in pregnancy, type 1 diabetes, and patients with CKD or hemoglobinopathies.

Self-Monitoring of Blood Glucose (SMBG)

Regular fingerstick glucose testing remains a fundamental tool, especially when structured testing protocols are used (e.g., pre- and post-meal paired testing, or a seven-point profile over one to two days). SMBG provides immediate feedback and allows patients to see the direct impact of food, activity, and medication. The limitations: it captures only discrete time points, can be burdensome, and adherence is often suboptimal. However, when combined with CGM or fructosamine, SMBG helps triangulate the true glycemic picture. For those without access to CGM, periodic structured SMBG profiling is a low-cost, high-yield strategy.

Emerging Approaches: A1c Correction Equations and GMI

Researchers have developed equations to adjust A1c for hemoglobin variants and race. For example, correction factors for HbS and HbC are available from some assay manufacturers. The Glucose Management Indicator (GMI), derived from CGM data, provides an estimated A1c that is not subject to red blood cell or hemoglobin interferences. These tools are increasingly integrated into diabetes management platforms. While not a perfect substitute, they add a layer of interpretative nuance. Rigorous validation in diverse populations remains an active area of investigation.

Practical Recommendations for Clinicians

To mitigate the risk of A1c underestimation (or overestimation) in vulnerable populations, clinicians should adopt the following systematic approach:

  • Maintain a high index of suspicion — When the A1c does not align with SMBG or CGM data, or when a patient has risk factors such as anemia, CKD, or hemoglobin variant trait, do not dismiss the discordance. Investigate before adjusting therapy.
  • Order a complete blood count and hemoglobin variant screen in patients with unexplained A1c–glucose discordance, particularly those of African, Mediterranean, or Southeast Asian descent. Knowing a patient has HbAS or HbC trait changes the interpretation of subsequent A1c values.
  • Consider a fructosamine or glycated albumin when A1c is unreliable. These tests are widely available through commercial laboratories. They are not a replacement but a complementary piece of evidence.
  • Use CGM liberally in high-risk populations, especially those with advanced CKD, sickle cell disease, or pregnancy. CGM-derived TIR provides actionable data that is independent of red blood cell pathology.
  • Document the monitoring strategy clearly in the medical record. If A1c is de-emphasized in favor of CGM or fructosamine, document the rationale and the target values being used. This is especially important for quality measures that rely on A1c thresholds.
  • Educate patients about why A1c might not tell the full story in their specific situation. Empowering patients with knowledge about alternative monitoring increases engagement and adherence.

For a more comprehensive review of monitoring options in diverse populations, the CDC Diabetes Getting Tested page offers accessible patient-facing information, while the NIH-supported research on racial differences in A1c provides deeper scientific context.

Conclusion

The A1c test is a powerful but imperfect tool. Its limitations in the face of hemoglobin variants, anemia, CKD, pregnancy, and racial/ethnic differences in glycation biology mean that a one-size-fits-all interpretation is not just inaccurate—it can be harmful. Clinicians who practice in diverse settings must move beyond reliance on a single number and embrace a multimodality approach that integrates SMBG, CGM, and alternative protein glycation markers. This shift requires awareness, education, and a willingness to question the numbers when they do not fit the clinical picture. By doing so, we can achieve more precise, equitable diabetes care that truly reflects each patient’s glycemic reality.