A Study of the Relationship between Genetic Mutations and Symptom Severity in Thalassemia Patients

Background:

β-thalassemia is a genetically heterogeneous disorder with variable clinical severity, influenced not only by mutations in the HBB gene but also by fetal hemoglobin (HbF)–modifying loci such as XmnI (HBG2), BCL11A, and HBS1L-MYB. The accurate prediction of disease severity remains a clinical challenge, particularly in resource-limited settings.

Objective:

This study aimed to evaluate whether a composite genotype score (CGS), integrating both HBB mutation class and HbF-modifying polymorphisms, could effectively discriminate between mild and severe β-thalassemia phenotypes. Additionally, we assessed the interaction between genetic determinants, hematologic parameters, hydroxyurea exposure, and clinical interventions.

Methods:

In a prospective case–control design, 50 genetically confirmed β-thalassemia patients (25 severe, 25 mild) were enrolled. Clinical severity was defined by standardized hemoglobin thresholds and transfusion dependence over two years. Genotyping was conducted using TaqMan assays and Sanger sequencing; HbF levels were quantified via HPLC. CGS and a quantitative severity index (QSI) were calculated from hematologic and genetic inputs. Statistical comparisons used independent-samples t-tests (with Welch’s correction for unequal variances) and Chi-square tests, with α=0.05. Predictive models were developed using logistic regression and validated internally.

Results:

Significant differences were found between severe and mild groups for pre-transfusion hemoglobin (6.9 vs. 9.9 g/dL), annual transfusion volume (188.5 vs. 38.8 mL/kg/year), and HbF% (13.7% vs. 25.2%) (all p < 0.001). CGS distribution was significantly associated with disease severity (p < 0.001). Hydroxyurea use and carriage of protective alleles showed trends toward milder phenotypes. The QSI demonstrated excellent group separation (mean 70.0 vs. 33.0, p < 0.001).

Conclusion:

The CGS effectively stratifies β-thalassemia patients by clinical severity, providing a low-cost, genomically informed approach to individualizing care. Incorporating genetic data into routine assessment can enhance early prediction, especially where imaging and advanced diagnostics are unavailable.