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Genotype-Phenotype Study of β-Thalassemia Patients in Sabah.

Latifah SualiFalah Abass Mohammad SalihMohammad Yusof IbrahimMohammad Saffree Bin JeffreeFiona Macniesia ThomasFong Siew MoyYap Shook FeEmma SualiSuhaini SudiCaroline Sunggip
Published in: Hemoglobin (2023)
β-thalassemia is a serious public health problem in Sabah due to its high prevalence. This study aimed to investigate the effects of different types of β-globin gene mutations, coinheritance with α-globin gene mutations, Xmn I- G γ, and rs368698783 polymorphisms on the β-thalassemia phenotypes in Sabahan patients. A total of 111 patients were included in this study. The sociodemographic profile of the patients was collected using a semi-structured questionnaire, while clinical data were obtained from their medical records. Gap-PCR, ARMS-PCR, RFLP-PCR, and multiplex PCR were performed to detect β- and α-globin gene mutations, as well as Xmn I- G γ and rs368698783 polymorphisms. Our data show that the high prevalence of β-thalassemia in Sabah is not due to consanguineous marriages (5.4%). A total of six different β-globin gene mutations were detected, with Filipino β°-deletion being the most dominant (87.4%). There were 77.5% homozygous β-thalassemia patients, 16.2% compound heterozygous β-thalassemia patients, and 6.3% β-thalassemia/Hb E patients. Further evaluation on compound heterozygous β-thalassemia and β-thalassemia/Hb E patients found no concomitant α-globin gene mutations and the rs368698783 polymorphism. Furthermore, the Xmn I- G γ (-/+) genotype did not demonstrate a strong impact on the disease phenotype, as only two of five patients in the compound heterozygous β-thalassemia group and two of three patients in the β-thalassemia/Hb E group had a moderate phenotype. Our findings indicate that the severity of the β-thalassemia phenotypes is closely related to the type of β-globin gene mutations but not to the Xmn I- G γ and rs368698783 polymorphisms.
Keyphrases
  • end stage renal disease
  • ejection fraction
  • public health
  • newly diagnosed
  • chronic kidney disease
  • peritoneal dialysis
  • machine learning
  • risk factors
  • big data