Exploring the Variability of A1c Results in Different Ethnic Groups

Understanding A1c Variability Across Ethnic Groups

The A1c test (hemoglobin A1c, HbA1c, or glycated hemoglobin) is a cornerstone of diabetes care. It estimates average blood glucose over the preceding 8–12 weeks and guides both diagnosis and treatment decisions for millions of people worldwide. Yet a growing body of research reveals that A1c results are not uniform across racial and ethnic groups. Even when actual blood glucose levels are equivalent—measured by continuous glucose monitors or oral glucose tolerance tests—individuals of African, Hispanic, Asian, and other non‑White backgrounds often show higher A1c values than White individuals. This variability can lead to under‑diagnosis, over‑diagnosis, or inappropriate treatment adjustments. For clinicians, public health officials, and patients, understanding the scope and drivers of these differences is essential to delivering equitable, personalized diabetes care.

Diabetes prevalence itself varies sharply by ethnicity. According to the Centers for Disease Control and Prevention (CDC), age‑adjusted rates of diagnosed diabetes in the United States are highest among American Indian/Alaska Native (14.5%), Hispanic (11.8%), and non‑Hispanic Black (11.7%) adults, compared with 7.4% among non‑Hispanic White adults. These disparities underscore the urgency of ensuring that diagnostic tools perform accurately across all populations. Variability in A1c results may compound existing inequities if not properly accounted for.

What the A1c Test Measures—and Its Limitations

The A1c test quantifies the percentage of hemoglobin molecules in red blood cells to which glucose has non‑enzymatically attached. Because red blood cells circulate for about 120 days, the A1c value provides a weighted average of blood glucose levels over that span. The American Diabetes Association (ADA) has long endorsed A1c ≥ 6.5% as a diagnostic threshold for diabetes, with levels between 5.7% and 6.4% indicating prediabetes.

However, the test is an indirect measure. Any factor that alters the lifespan of red blood cells, the rate of glucose attachment (glycation), or the structure of hemoglobin itself can shift A1c readings independently of true glycemia. These factors are not evenly distributed across ethnic groups, which is the root of the variability problem.

Other limitations include the test’s inability to capture glycemic variability—peaks and troughs in glucose—and its lower sensitivity in certain conditions such as anemia, kidney disease, or pregnancy. For these reasons, the A1c test is best interpreted in context, not as a standalone number.

Evidence of Ethnic Differences in A1c

African American and Black Populations

One of the most well‑documented patterns is the tendency for A1c to be higher in African American and Black individuals compared with White individuals at the same measured glucose levels. A landmark study published in the Journal of the American Medical Association (JAMA) analyzed data from the National Health and Nutrition Examination Survey (NHANES) and found that, after adjusting for age, body mass index, and other confounders, African Americans had A1c values approximately 0.4% higher than Whites with equivalent fasting glucose. Subsequent research using continuous glucose monitoring (CGM) data confirmed that the mean glucose‑to‑A1c relationship differs significantly by race.

For example, a 2020 study by Bergenstal et al. in the Journal of Clinical Endocrinology & Metabolism examined participants with type 1 diabetes who wore CGMs for 12 weeks. The authors reported that to achieve the same A1c of 7.0%, African American participants required a mean glucose level about 15–20 mg/dL higher than White participants—meaning their A1c overestimated their true average glucose. This finding suggests that using uniform A1c thresholds may lead to unnecessary intensification of glucose‑lowering therapy in Black patients, raising the risk of hypoglycemia.

Hispanic and Latino Populations

Similar, though sometimes less pronounced, differences have been found in Hispanic and Latino groups. In NHANES data, Mexican‑American participants had slightly higher A1c values than non‑Hispanic Whites at the same glucose levels. However, the gap is smaller than that observed in African Americans. The heterogeneity within Hispanic populations—including varying proportions of Indigenous American, European, and African ancestry—likely contributes to the range of findings. Studies that adjust for socioeconomic status and acculturation still find an independent racial/ethnic effect, pointing to biological rather than purely social causes.

Asian and South Asian Populations

Research on Asian populations is less extensive, but several studies indicate that A1c may systematically underestimate glycemia in people of East Asian and South Asian descent. For instance, a 2018 meta‑analysis in Diabetes Care found that, at any given A1c level, South Asians had lower fasting glucose and post‑prandial glucose compared with Whites. This pattern suggests that using standard A1c cutoffs could miss diabetes diagnoses in Asian individuals—the opposite problem from that seen in African Americans. The reasons likely relate to differences in hemoglobin glycation rates and red blood cell physiology.

Indigenous and Other Populations

Fewer data are available for Indigenous groups such as Native American, Alaska Native, and Pacific Islander populations, but some reports suggest elevated A1c values relative to glucose levels. Given the high diabetes prevalence in many of these communities, further research is urgently needed.

Why Does A1c Vary by Ethnicity?

Genetic Differences in Hemoglobin and Glycation

Not all hemoglobin is identical. Hundreds of hemoglobin variants exist, many of which are more common in specific ethnic groups. For example, hemoglobin S (sickle cell trait) and hemoglobin C are prevalent in individuals of African descent, while hemoglobin E is common in Southeast Asian populations. Some variants can interfere with the laboratory assay used to measure A1c, leading to falsely high or low results. Even when using high‑performance liquid chromatography (HPLC) or immunoassays that attempt to correct for variants, subtle effects may persist.

Beyond variants, genetic polymorphisms affect the rate at which glucose attaches to hemoglobin (the glycation rate). The enzyme fructosamine‑3‑kinase (FN3K) influences deglycation, and variations in its activity may differ by ancestry. Studies of African American families suggest a heritable component to A1c that is independent of blood glucose.

Red Blood Cell Lifespan

A1c reflects the fraction of glycated hemoglobin over the average red blood cell lifespan. If red blood cells live longer than the typical 120 days, more time exists for glucose to accumulate, producing a higher A1c for the same mean glucose. Conversely, shorter lifespan lowers A1c. Research indicates that African Americans may have slightly longer red blood cell survival on average compared with Whites, while some Asian populations may have shorter survival. These differences, though small, are enough to shift A1c by 0.2–0.4%.

Iron Deficiency and Anemia

Iron deficiency anemia is more prevalent among African Americans and some other minority groups, in part due to dietary and socioeconomic factors. Iron deficiency itself can elevate A1c, independently of glycemia, because the body produces more hemoglobin in immature red blood cells, which are more susceptible to glycation. Treating iron deficiency often lowers A1c, unmasking that the original value was artifactually high.

Socioeconomic and Healthcare Access Factors

While biological mechanisms are central, social determinants of health cannot be ignored. Populations with limited access to healthcare may have less consistent diabetes monitoring, higher stress levels, and dietary patterns that affect glycemic control. However, the persistence of ethnic A1c differences in well‑controlled studies that adjust for income, education, and access suggests that biological factors are real and independent contributors. Still, clinicians must recognize that social context influences the overall picture of diabetes risk and control.

Implications for Clinical Practice

Risk of Misdiagnosis and Overtreatment

The most immediate consequence of ethnic A1c variability is the potential for diagnostic errors. If the same A1c threshold (e.g., 6.5%) is applied universally, individuals of African ancestry may be diagnosed with diabetes at lower true glucose levels, and conversely, some individuals of Asian ancestry may be missed. In a 2021 analysis, researchers estimated that using a race‑modified A1c threshold (6.3% for African Americans) could reclassify a substantial number of people, reducing misdiagnosis.

In treatment settings, reliance on A1c alone can lead to overtreatment in Black patients—intensifying medication based on an A1c that overestimates glycemia—while undertreating Asian patients. Both errors are harmful: overtreatment increases hypoglycemia risk, and undertreatment raises the likelihood of long‑term complications.

Need for Complementary Testing

Given these limitations, many experts advocate for using additional metrics alongside A1c, particularly when evaluating individuals from ethnic groups where variability is known. Options include:

Fasting plasma glucose (FPG): Provides a snapshot of glucose homeostasis; combined with A1c, it improves diagnostic accuracy.
Oral glucose tolerance test (OGTT): Measures the body’s response to a glucose load, offering insight into post‑prandial control.
Continuous glucose monitoring (CGM): Provides a direct measure of mean glucose and glycemic variability over 10–14 days. CGM‑derived metrics, such as the glucose management indicator (GMI), can be aligned with A1c and may reduce race‑based discrepancies.
Fructosamine or glycated albumin: These reflect shorter‑term (2–3 week) glycemic control and are not affected by hemoglobin variants or red blood cell lifespan. Their correlation with mean glucose also appears to vary by ethnicity but may offer a useful alternative in selected cases.

The ADA’s Standards of Medical Care in Diabetes acknowledges these concerns and recommends that clinicians consider the possibility of discordance between A1c and true glycemia, especially in patients with conditions that affect red blood cell turnover or with known hemoglobin variants. However, the ADA has not yet endorsed routine race‑specific diagnostic cutoffs, citing the need for more evidence and the complexity of implementing such changes.

Personalized Diabetes Care

Ultimately, the management of diabetes must be individualized. For a patient with an elevated A1c who also has a family history of diabetes, obesity, or other risk factors, the diagnosis is rarely in doubt. But when A1c is borderline or discordant with self‑monitored glucose readings or clinical presentation, the clinician should consider obtaining an FPG, OGTT, or CGM to confirm. Awareness of ethnic variability is a tool for shared decision‑making: explaining to patients that A1c can differ by race may improve trust and adherence.

Current Recommendations and Controversies

The question of whether to adopt race‑specific A1c thresholds remains hotly debated. Proponents argue that the evidence is strong enough to justify a lower threshold for diagnosis in East Asian populations and a higher threshold in African Americans. Opponents worry that race‑based adjustments could perpetuate stereotypes, ignore the heterogeneity within racial categories, and lead to confusion in clinical practice. Moreover, race itself is a social construct, not a precise biological variable; ancestry‑informed analysis may be more accurate but is rarely available in routine care.

The Endocrine Society and the American Association of Clinical Endocrinology have called for additional research but, as of 2025, still endorse the standard A1c cutoffs for all adults. Meanwhile, the International Expert Committee on Diabetes Criteria has acknowledged that ethnic differences exist and advises caution in interpreting A1c in certain populations.

One potential middle ground is to advocate for more widespread use of CGM, which directly measures glucose and sidesteps many of the confounding factors that affect A1c. As CGM technology becomes more affordable and accessible, it may reduce the reliance on A1c as the sole diagnostic and monitoring tool, particularly in primary care settings.

Future Directions in Research

Ongoing studies are using large biobanks, genome‑wide association studies (GWAS), and longitudinal CGM data to disentangle the genetic and epigenetic factors that influence A1c. The National Institutes of Health (NIH) has funded initiatives like the “All of Us” research program, which aims to enroll a diverse cohort of one million Americans. Data from such efforts may eventually lead to personalized algorithms that incorporate ancestry, red blood cell indices, and hemoglobin profiles to calibrate A1c interpretation.

Additionally, research into alternative glycemic markers—such as glycated albumin, which is unaffected by hemoglobin or red blood cell lifespan—may provide more equitable options. However, glycated albumin also shows some ethnic variation and is influenced by albumin metabolism, limiting its immediate utility. The next decade will likely see the emergence of multi‑marker approaches that combine A1c, CGM data, and other biomarkers for a comprehensive assessment of glycemic control.

Conclusion: Toward Equitable Diabetes Care

The variability of A1c results among ethnic groups is a well‑established phenomenon with real‑world consequences for millions of patients. A one‑size‑fits‑all approach to diabetes diagnosis and management is no longer tenable in diverse societies. Clinicians must be educated about the potential for A1c to both overestimate and underestimate glycemia depending on the patient’s ancestry, and they should have access to confirmatory testing when discrepancies arise. For researchers, the challenge is to translate findings on hemoglobin kinetics, glycation rates, and genetic variants into practical clinical tools that can be implemented broadly.

Ultimately, recognizing ethnic variability in A1c is not about diminishing the value of the test—it remains a powerful, convenient, and cost‑effective tool—but about using it wisely. By integrating complementary measures and maintaining a high index of suspicion for discordance, healthcare providers can deliver more accurate diagnoses, safer treatments, and better outcomes for everyone. The goal is personalized medicine that respects the biological and social complexity of the populations we serve.