Introduction: The Role of A1c in Diabetes Management

The hemoglobin A1c (A1c) test is a cornerstone of diabetes care, providing a reliable estimate of average blood glucose over the preceding two to three months. Since its introduction in the 1970s, it has become the primary metric for assessing glycemic control, guiding treatment adjustments, and predicting the risk of diabetic complications. Organizations such as the American Diabetes Association (ADA) recommend A1c targets of below 7% for most adults with diabetes.

However, the test’s widespread use masks a critical limitation: its accuracy depends on normal hemoglobin structure and a consistent red blood cell lifespan. In populations with inherited hemoglobin disorders—hemoglobinopathies such as sickle cell disease, thalassemias, and sickle cell trait—these assumptions break down. The result can be A1c readings that are falsely high or low, leading to misclassification of glucose control and potentially harmful clinical decisions.

This article explores the mechanisms behind A1c inaccuracy in patients with hemoglobinopathies and sickle cell trait, discusses the clinical implications, and reviews alternative monitoring strategies that provide more reliable data in these populations.

Hemoglobinopathies and Sickle Cell Trait: An Overview

What Are Hemoglobinopathies?

Hemoglobinopathies are inherited disorders that alter the structure or production of hemoglobin. The most common clinically significant types include:

  • Sickle cell disease (HbSS, HbSC, HbSβ-thalassemia)—caused by a point mutation replacing glutamate with valine at position 6 of the beta-globin chain, producing hemoglobin S (HbS).
  • Thalassemias—caused by reduced or absent synthesis of either alpha-globin (alpha-thalassemia) or beta-globin (beta-thalassemia) chains, leading to microcytic anemia and imbalanced chain production.
  • Hemoglobin C (HbC), E (HbE), and D (HbD) variants, common in specific geographic regions (West Africa, Southeast Asia, parts of the Middle East).

Globally, hemoglobinopathies affect millions of people. The World Health Organization (WHO) estimates that 5% of the world’s population carries a hemoglobin variant gene. Sickle cell disease alone affects approximately 300,000 newborns annually, predominantly in sub-Saharan Africa. Thalassemia carriers account for about 1.5% of the global population.

Sickle Cell Trait: Carrier State with Clinical Consequences

Sickle cell trait (HbAS) occurs when an individual inherits one normal beta-globin gene (HbA) and one sickle cell gene (HbS). Typically considered a benign carrier state, it carries a low risk of complications such as exertional rhabdomyolysis or splenic infarction. However, its effect on A1c measurement is not negligible. Approximately 1 in 12 African Americans carries the sickle cell trait, making it a common confounder in diabetes monitoring within this demographic.

Other hemoglobinopathies such as HbC and HbE trait are also encountered frequently in clinical practice, especially in immigrant populations. These conditions alter the hemoglobin structure without causing the severe anemia seen in homozygous disease, yet they still interfere with A1c testing.

Mechanisms of A1c Inaccuracy in Hemoglobinopathies

What Does the A1c Test Actually Measure?

The A1c test measures the percentage of hemoglobin molecules that have glucose molecules irreversibly attached to the N-terminal valine of the beta-globin chain via a non-enzymatic glycation reaction. The rate of glycation depends on the average glucose concentration and the red blood cell’s exposure time. Therefore, any condition that alters the structure of hemoglobin or the lifespan of red blood cells will interfere with the result.

Altered Red Blood Cell Lifespan

In sickle cell disease, the red blood cell (RBC) lifespan is markedly shortened—from the normal ~120 days down to 10–30 days. Because A1c accumulates over the cell’s lifetime, a shorter lifespan leads to less time for glycation, producing an A1c that is falsely low relative to the actual mean glucose. In thalassemia, the RBC lifespan can also be reduced due to ongoing hemolysis, again lowering A1c.

Conversely, in some hemoglobinopathies with prolonged RBC survival (e.g., some forms of HbC disease), A1c can be falsely elevated. The effect is not uniform; it depends on the specific variant and the degree of hemolysis or erythropoietic compensation.

Interference with Laboratory Assay Methods

Modern A1c assays use various analytical methods—ion-exchange high-performance liquid chromatography (HPLC), immunoassay, capillary electrophoresis, and enzymatic methods. Each has different vulnerabilities to hemoglobin variants:

  • Ion-exchange HPLC: This separates hemoglobins based on charge. Variants like HbS, HbC, and HbF can co-elute with HbA1c, producing either a falsely elevated peak or an artefactual shoulder that confounds integration. Many HPLC systems now include variant detection warnings, but some fail to quantify A1c correctly.
  • Immunoassays: These rely on antibodies that recognize the glycated N-terminal peptide of the beta-globin chain. If the variant alters this epitope (as in HbC or HbE), the antibody binding may be impaired, producing a falsely low A1c.
  • Enzymatic methods: These use enzymes that cleave the glycated or total hemoglobin; they are less sensitive to structural variants but can still be affected in the presence of high HbF or HbS levels.
  • Capillary electrophoresis: This method offers better separation of variants and can often identify the presence of an abnormal hemoglobin peak, alerting the clinician to potential interference.

The National Glycohemoglobin Standardization Program (NGSP) provides a list of methods that have been evaluated for interference. Nevertheless, interference profiles are constantly updated, and clinicians must remain aware that no method is immune to all variants.

Specific Variants and Their Effects on A1c

Hemoglobin Variant Common Geographic Distribution Effect on A1c (if test method not validated)
HbS (sickle cell trait) Sub-Saharan Africa, African diaspora, parts of India, Mediterranean Falsely low (trait) or low (disease); interference with HPLC
HbC (trait or disease) West Africa, African diaspora Falsely low with immunoassays; HPLC may produce a separate peak
HbE (trait or disease) Southeast Asia (esp. Thailand, Cambodia, Laos) Falsely low with immunoassays and some HPLC systems
HbF (elevated in hereditary persistence of fetal hemoglobin, some thalassemias) Worldwide (higher frequency in certain Mediterranean/Middle East populations) Falsely low (dilutional effect) with some assays; may prolong RBC lifespan
HbD (Punjab variant) Punjab region (India, Pakistan), Caucasian individuals Falsely low or no effect depending on method; can co-elute with HbA1c on HPLC

Clinical Implications of Misleading A1c Values

Risk of Overtreatment or Undertreatment

When A1c is falsely low in a patient with sickle cell trait or disease, the clinician may believe glycemic control is excellent when in fact glucose levels are elevated. This can lead to undertreatment—failure to intensify insulin or other medications—increasing the risk of long-term microvascular and macrovascular complications. Conversely, a falsely high A1c (as may occur in HbC disease or from interference in certain assays) can prompt excessive treatment, raising the risk of hypoglycemia.

For example, a study in African Americans with sickle cell trait found that A1c underestimated the mean glucose concentration by 0.3–0.5% on average (Lacy et al., 2018). While this may seem modest, at a population level it could shift many individuals from the target range into a zone of inadequate control.

Impact on Screening and Diagnosis

The ADA suggests that A1c ≥ 6.5% can be used for diabetes diagnosis. However, in populations with a high prevalence of hemoglobinopathies—such as sub-Saharan Africa, the Caribbean, Southeast Asia, and among African Americans—a low A1c may mask prediabetes or diabetes, delaying intervention. Falsely high A1c can lead to overdiagnosis, causing unnecessary anxiety and treatment.

Disparities in Diabetes Care

Hemoglobinopathies are more common in minority and underserved populations. The misclassification of glycemic control due to A1c interference exacerbates existing health disparities. A patient who relies solely on A1c may be denied appropriate medication intensification or may be labeled as poorly controlled when in fact glucose is stable. Understanding these limitations is a key step toward equitable diabetes management.

Alternative Monitoring Strategies for Accurate Glycemic Assessment

Fructosamine Testing

Fructosamine measures the total glycated protein (primarily albumin) in the blood, reflecting average glucose levels over the preceding 1–3 weeks. Because it does not depend on hemoglobin, it is unaffected by hemoglobinopathies. However, it is influenced by albumin levels—common in patients with chronic disease, malnutrition, or nephrotic syndrome. Fructosamine can provide useful adjunctive data when A1c is unreliable.

Glycated Albumin

A more specific measure than total fructosamine, glycated albumin (GA) measures the percentage of albumin molecules that have been glycated. It has a shorter time frame (about 2–3 weeks) and is less affected by albumin turnover. GA has been shown to correlate better with CGM-derived glucose than A1c in patients with hemoglobinopathies. However, it is not yet as widely standardized as A1c, and cost may limit its use in some settings.

Self-Monitoring of Blood Glucose (SMBG)

Frequent fingerstick glucose testing remains a mainstay for day-to-day insulin dosing. For patients with hemoglobinopathies, SMBG is essential to confirm that the A1c reading aligns with the glucose log. The challenge is that SMBG provides snapshots rather than a continuous picture, but when done systematically (e.g., paired before-and-after meals, overnight), it can yield a reliable estimate of average glucose.

Continuous Glucose Monitoring (CGM)

CGM devices measure interstitial glucose every 5–15 minutes, offering a rich dataset for calculating metrics such as time-in-range (TIR), mean glucose, and glycemic variability. TIR (typically percentage of readings between 70–180 mg/dL) has been correlated with A1c and is now endorsed by international consensus for use in clinical trials and practice. For patients with hemoglobinopathies, CGM is the gold-standard alternative to A1c, as it does not rely on hemoglobin or RBC lifespan. However, CGM access remains limited by cost, insurance coverage, and patient preference.

Note: An expert consensus from 2019 recommends that in patients with hemoglobin variants, if A1c is discordant with SMBG or CGM data, the non-A1c measurement should be used to guide therapy.

Calculating Derived Glucose Metrics

The estimated average glucose (eAG) can be calculated from A1c, but in hemoglobinopathies this conversion assumes a normal RBC lifespan. Some laboratories report an A1c with a warning flag when a variant is detected. Clinicians can also compare the A1c-derived eAG against actual glucose records (from CGM or frequent fingersticks). When the difference exceeds 10–15%, A1c should be considered unreliable.

Practical Recommendations for Clinicians

1. Identify Patients at Risk

Ask about ethnicity, family history of anemia or hemoglobin disorders, and prior lab reports indicating a variant. Patients from populations at high risk for hemoglobinopathies (African, Mediterranean, Middle Eastern, Southeast Asian) should be screened when A1c results are used for diagnosis or monitoring. A simple hemoglobin variant screen (e.g., HPLC, isoelectric focusing) can confirm or exclude common variants.

2. Use a Validated Assay

Consult the NGSP interference tables to select an A1c method that has been tested for the relevant variant. If the patient has a known hemoglobinopathy, avoid immunoassays for HbC, HbE, and HbS. Some laboratories automatically run a reflex test (e.g., capillary electrophoresis) when an abnormality is detected, which can quantify the variant and in many cases still report a reliable A1c.

3. Correlate with Other Measures

For any patient with a hemoglobin variant, rely on a combination of A1c (if method-validated), SMBG logs, and if possible, CGM or fructosamine. If the A1c conflicts with glucose data, prioritize the glucose-based metrics. Document this discordance in the medical record to avoid future misinterpretation.

4. Consider the Patient’s Clinical Context

In patients with sickle cell disease who experience frequent transfusions, A1c is completely unreliable because transfused RBCs have normal lifespan. In such cases, CGM or fructosamine are mandatory. For patients with sickle cell trait who have stable hemoglobin levels, A1c may be acceptable if the assay method is known to be accurate for HbS. However, a validation study showed that only about half of the NGSP-listed methods are currently certified for use with HbAS, so check current evidence.

Conclusion

Hemoglobin A1c is a robust tool for the vast majority of diabetes patients, but its limitations in individuals with hemoglobinopathies and sickle cell trait are profound and well-documented. Abnormal hemoglobin variants, shortened RBC lifespan, and assay interference can produce A1c values that are misleadingly low or high, leading to clinical misjudgments. In populations where these conditions are prevalent, reliance on A1c alone perpetuates disparities in diabetes care.

Clinicians must proactively identify at-risk patients, select validated laboratory methods, and incorporate alternative monitoring strategies such as fructosamine, glycated albumin, SMBG, or CGM. Collaboration between primary care, endocrinology, and hematology can ensure that glycemic management is based on accurate data. Ongoing education and updated guidelines from the ADA and international societies are essential to improving outcomes for this vulnerable patient group.

By acknowledging the limitations of A1c and adapting monitoring approaches, healthcare providers can achieve equitable, precise, and safe diabetes care for every patient, regardless of their hemoglobin genotype.