Variability In Hb A2 levels among Individuals with Beta-Thalassemia Trait: Is Iron Deficiency Associated with Abnormally Low Hb A2?
Bibliographic record
Abstract
Abstract Abstract 4281 Introduction: Quantification of HbA2 is a well established screening test for beta-thalassemia trait (BTT). However, there is a small subset of individuals heterozygous for beta-thalassemia mutations who have HbA2 levels less than or equal to 3.5%. Some reports have suggested that iron deficiency in BTT patients causes HbA2 to be lower than expected, while others have found no significant relationship between iron deficiency and the level of HbA2. These conflicting reports have led to confusion amongst clinicians as to the reliability of HbA2 measurement when screening for BTT in iron deficient individuals. Methods: From a database of 444 individuals with heterozygous beta-thalassemia, confirmed by molecular testing, we assessed the variability in HbA2. Individuals were classified as “iron deficient” or “non-iron deficient” based on their serum ferritin, using two definitions of iron deficiency (serum ferritin <15 ug/L or <30 ug/L), and data was analyzed independently. The association of HbA2 levels with gender, iron deficiency or beta-thalassemia mutation type was evaluated using a two-sample T-test. The relationship of HbA2 with gender, hemoglobin level, Hb F percentage, reticulocyte percentage and/or the natural logarithm of serum ferritin was evaluated using single and multiple linear regression analysis. Results: HbA2 ranged from 2.0 to 8.1%, with a mean of 5.5%. Mean HbA2 in females (5.4%, SD 0.6%) was lower than that in males (5.6%, SD 0.6%) (p=0.003). Seven individuals had a HbA2 level below 3.5% (range 2.0–3.1%), all of whom had serum ferritin ≥15 ug/L. Of these people, five had the Chinese (Aγδβ)0-thalassemia deletion, and would thus be expected to have a normal HbA2 level; one woman had a HbA2 of 3.1% and was found to be heterozygous for a novel point mutation at codon 29 of the delta-globin gene (GGC>GAC or Gly29Asp); and another woman with HbA2 of 2.0% had unexplained elevation of Hb F (8.0%). The 30 patients with serum ferritin <15 ug/L had evidence of iron-deficient hematopoiesis (significantly lower hemoglobin concentration and MCV than patients with ferritin ≥ 15), and none had HbA2 <3.5%. However, HbA2 was lower (5.3%) versus HbA2 in non-iron deficient individuals (5.5%; p=0.04). Individuals with the Chinese (Aγδβ)0-thalassemia deletion had a mean Hb A2 of 2.8%. Of the remaining beta-thalassemia genotypes, IVSII-645 and Nt -29 beta+ mutations were associated with HbA2 values significantly below the group mean, and -28 beta+ and the Codon 17, Codons 41/42 and Codon 43 beta0 mutations were associated with significantly higher HbA2 values. Multiple linear-regression analysis demonstrated a significant association of low HbA2 with low ferritin (p=0.04) and beta+-thalassemia mutation type (p<0.001). There was no significant association with gender (p=0.1), hemoglobin (p=0.2), reticulocyte count (p=0.08) or hemoglobin F level (p=1.0). Conclusions: From our population of patients with BTT, we have shown that individuals with overt iron deficiency all had HbA2 within the range expected for an individual with BTT (Hb A2 >3.5%). Nonetheless, serum ferritin < 15 ug/L was associated with a small but significant decrease in HbA2 (mean HbA2 of 5.3%, versus 5.5% in non-iron deficient individuals; p=0.04). Although a multivariate analysis confirmed that ferritin under 15 ug/L was associated with lower HbA2 levels, beta+-thalassemia mutation type explained more of the variability than did iron status in individuals with lower HbA2 values. Six out of seven patients who had HbA2 <3.5% were found to have heterozygous deletion or mutation of the delta-globin gene. Based on the results of our study, HbA2 remains a reliable test for BTT screening, even in the presence of iron deficiency. In patients known or suspected to have BTT, but in whom HbA2 is found to be <3.5%, testing for delta-globin abnormalities should be pursued. Disclosures: No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".