Sickle Cell Trait May Affect Diabetes Test Accuracy
- Sickle cell trait can cause inaccurate results in certain diabetes tests, according to research presented at the Association for the Diagnosis of Laboratory Medicine (ADLM) 2026 meeting in...
- Healthcare-in-Europe reported on July 29, 2026, that the study highlights a potential diagnostic gap for individuals carrying the sickle cell trait.
- This interference can lead to clinicians misinterpreting a patient's glycemic control, potentially resulting in incorrect treatment adjustments or missed diagnoses of diabetes.
Sickle cell trait can cause inaccurate results in certain diabetes tests, according to research presented at the Association for the Diagnosis of Laboratory Medicine (ADLM) 2026 meeting in Anaheim. The findings suggest that the presence of the trait may interfere with the measurement of glycated hemoglobin (HbA1c), a standard marker used to monitor long-term blood glucose levels.
Healthcare-in-Europe reported on July 29, 2026, that the study highlights a potential diagnostic gap for individuals carrying the sickle cell trait. Because HbA1c tests rely on the attachment of glucose to hemoglobin, variations in hemoglobin structure—such as those found in sickle cell trait—can distort the final reading.
This interference can lead to clinicians misinterpreting a patient’s glycemic control, potentially resulting in incorrect treatment adjustments or missed diagnoses of diabetes.
The research was presented during the ADLM 2026 conference, where laboratory professionals and pathologists gather to discuss advancements in diagnostic accuracy and clinical chemistry.
How Sickle Cell Trait Affects HbA1c Testing
The HbA1c test measures the percentage of hemoglobin that is glycated over the previous two to three months. According to the research presented at ADLM 2026, the sickle cell trait—where a person inherits one hemoglobin S gene—can alter the lifespan of red blood cells or the way glucose binds to the hemoglobin molecule.
When red blood cells have a shorter lifespan or abnormal hemoglobin structures, the HbA1c level may appear lower than the actual average blood glucose would suggest. This creates a discrepancy between the test result and the patient’s true metabolic state.
The study presented in Anaheim emphasizes that this is not a characteristic of sickle cell disease itself, but specifically how the trait interacts with the chemistry of common laboratory assays.
Clinical Implications for Diabetes Management
The primary risk identified in the findings is the potential for false-negative or underestimated results. For a patient with diabetes, an artificially low HbA1c reading might lead a provider to believe a treatment plan is working when blood sugar levels remain dangerously high.
Conversely, the research suggests that the lack of reliability in these tests for trait carriers may necessitate the use of alternative monitoring methods. These alternatives often include fructose amines or continuous glucose monitoring (CGM), which do not rely on hemoglobin levels to determine glucose averages.
The presentation at the ADLM 2026 meeting suggests that awareness of a patient’s genetic hemoglobin profile is critical for accurate diagnostic interpretation in populations with a higher prevalence of sickle cell trait.
Addressing Diagnostic Gaps in Public Health
The findings presented in Anaheim underscore a broader challenge in precision medicine: the need for diagnostic tools that are accurate across diverse genetic backgrounds. Because sickle cell trait is more common in people of African, Mediterranean, and Middle Eastern descent, the interference in diabetes testing disproportionately affects these groups.
The research suggests that laboratory systems may need to implement screening or flags for patients with known hemoglobinopathies to ensure that clinicians do not rely solely on HbA1c for diabetes management.
By identifying the specific interference caused by the sickle cell trait, the study provides a pathway for laboratories to refine their testing protocols and for physicians to select more appropriate biomarkers for at-risk populations.
