Carrier rate of thalassemia among 25,910 high school students in Shaoguan area, China

OBJECTIVES

As one of the most common hereditary diseases, thalassemia affects a large number of people in China. The aim of this study was to investigate the feasibility of a method based on next-generation sequencing (NGS) for screening of thalassemia carriers among high school students in the Shaoguan area.

MATERIALS AND METHODS

The NGS-based method was performed using 25,910 high school students recruited from 38 schools. The screening yield was systematically analyzed. Before screening, a lecture on how the disease is inherited, the symptoms of thalassemia, and how to prevent it was given to 28,780 students.

RESULTS

Implying successful delivery of information on the disease, 90.03% (25,910 of 28,780) of the students agreed to join this program for thalassemia screening. A thalassemia carrier rate of 15.99% (4144 of 25,910) was found. Also, 69 rare genotypes (28 of α-thalassemia and 41 of β-thalassemia) and 9 novel variants were identified.

CONCLUSIONS

This NGS-based method provided a feasible platform for high school population thalassemia screening. Combined with a clinical follow-up strategy, it could help eventually to prevent the births of affected children.

Prenatal Screening and Diagnosis of ß-Thalassemia in India: Is ARMS-PCR Enough?

Accurate and timely prenatal diagnosis of thalassemia is cornerstone to the success of thalassemia control; currently parents are screened for ß-thalassemia mutations by ARMS-PCR and subsequently chorionic villus sampling is done. We did an audit to ascertain whether the present design is adequate and determined the role of sequencing for pre-natal diagnosis of beta-thalassemia. This was a retrospective analysis of prenatal testing data collected over 10 years, (2010-2019). ARMS-PCR was done to identify the beta-globin mutation followed by CVS wherever indicated. Data was classified into 3 groups:-5 most commonly occurring mutations (group 1), less common mutations (group 2) and mutations not detected (group 3). Total number of cases studied were 2128. Mean age of the cohort was 29.30 years (range 18-48 years). Approximately 90% individuals had one of the 5 common mutations in decreasing order of frequency: IVS 1-5 G>C (1297/2128); Codon 26G>A/HbE (451/2128); codon 30G>C (69/2128); codon 15G>A (61/2128); FS 41-42-CTTT (48/2128). Undetected mutations amounted to 7.3% (156/2128). Mean haemoglobin was highest in the group 2 (12.46 g/dl) followed by the group 1 (11.20 g/dl) and least in group 3 (10.99 g/dl). MCV, MCH and MCHC showed similar trends. ANOVA on all these parameters, except RDW, within groups and for individual mutations, were statistically significant (p < 0.001). The hemogram-HPLC-ARMS-PCR-CVS approach is a cost-effective and established method but tends to miss out a considerable number of thalassemia mutations (~7%), emphasizing the role of sequencing in difficult cases. This needs to be addressed while formulating guidelines for thalassemia screening in future.

A rapid quantification method for the screening indicator for β-thalassemia with near-infrared spectroscopy

Near-infrared (NIR) spectroscopy combined with chemometrics was applied to rapidly analyse haemoglobin A2 (HbA₂) for β-thalassemia screening in human haemolysate samples. The relative content indicator HbA₂ was indirectly quantified by simultaneous analysis of two absolute content indicators (Hb and Hb∙HbA₂). According to the comprehensive prediction effect of the multiple partitioning of calibration and prediction sets, the parameters were optimized to achieve modelling stability, and the preferred models were validated using the samples not involved in modelling. Savitzky-Golay smoothing was firstly used for the spectral pretreatment. The absorbance optimization partial least squares (AO-PLS) was used to eliminate high-absorption wave-bands appropriately. The equidistant combination PLS (EC-PLS) was further used to optimize wavelength models. The selected optimal models were I=856nm, N=16, G=1 and F=6 for Hb and I=988nm, N=12, G=2 and F=5 for Hb∙HbA₂. Through independent validation, the root-mean-square errors and correlation coefficients for prediction (RMSEP, R_P) were 3.50gL^-1 and 0.977 for Hb and 0.38gL^-1 and 0.917 for Hb∙HbA₂, respectively. The predicted values of relative percentage HbA₂ were further calculated, and the calculated RMSEP and R_P were 0.31% and 0.965, respectively. The sensitivity and specificity for β-thalassemia both reached 100%. Therefore, the prediction of HbA₂ achieved high accuracy for distinguishing β-thalassemia. The local optimal models for single parameter and the optimal equivalent model sets were proposed, providing more models to match possible constraints in practical applications. The NIR analysis method for the screening indicator of β-thalassemia was successfully established. The proposed method was rapid, simple and promising for thalassemia screening in a large population.

Known and new hemoglobin A2 variants in Thailand and implication for β-thalassemia screening

BACKGROUND

We reported molecular and hematological characteristics of δ-globin chain variants and addressed diagnostic consideration of complex hemoglobinopathies caused by their interactions with α- and β-thalassemias.

METHODS

Study was done on four unrelated Thai subjects with second Hb A2 fractions. Hb analysis was carried out using automated HPLC and capillary electrophoresis. Mutations were identified by DNA analysis. Novel diagnostic methods based on PCR-RFLP and allele specific PCR were developed.

RESULTS

Hb analysis revealed Hb A2 variant in all cases. DNA analysis of δ-globin gene identified the Hb A2-Melbourne [δ43(CD2)Glu→Lys] in combination with α(+)-thalassemia, α(0)-thalassemia and β(0)-thalassemia in the first three cases, respectively. Analysis of the remaining case identified a novel δ-Hb variant namely the Hb A2-Lampang [δ47(CD6)GAT→AAT; Asp→Asn] found in association with Hb E and α(+)-thalassemia. These mutations could be identified using PCR-RFLP and allele specific PCR assays developed.

CONCLUSIONS

It is necessary to recognize the Hb A2 variant and to combine the amounts of Hb A2 and Hb A2-variant for a total Hb A2 value to make better diagnostic of these complex syndromes. Co-inheritance of these multiple globin gene defects could lead to complex hemoglobinopathies requiring comprehensive Hb and molecular assessments.

Impact of iron deficiency on hemoglobin A2% in obligate β-thalassemia heterozygotes

INTRODUCTION

The potential impact of concomitant iron deficiency on hemoglobin A2 (HbA2)-based identification of β-thalassemia trait (βTT) is a worrisome issue for screening laboratories. This is especially true for resource-constrained settings where iron deficiency is widespread and molecular confirmatory tests for borderline low HbA2 values may be unavailable.

METHODS

Obligate βTT carrier individuals (n = 752) were identified during screening studies on the parents of thalassemia major patients. HbA2%, complete blood counts and serum iron, ferritin and transferrin saturation were studied. Iron-deficient individuals (n = 135) with normal range HbA2% were taken as controls.

RESULTS

Concomitant iron deficiency (defined as ferritin ≤15 ng/mL and/or transferrin saturation ≤15%) was present in 20.7% (156/752) βTT cases, that is, 33.3% females (122/366) and 8.8% males with βTT (34/386). Mean HbA2 in iron-replete βTT was 5.4 ± 0.8 (range 3.1-7.9) and in iron-deficient βTT was 5.4 ± 0.9 (range 3.3-7.6). HbA2 < 4.0% was found in 23/752 (3.1%) βTT: 13/595 iron-replete (2.2%) and 10/157 (6.4%) iron-deficient βTT individuals. However, five of the 10 iron-deficient βTT cases carried the silent CAP+1 (A>C) β-thalassemia allele accounting for the borderline HbA2%. On a separate analysis, all five severely anemic βTT (Hb < 80 g/L) and 16/17 βTT with severe hypoferritinemia (<5 ng/mL) had HbA2 > 4.5%. The single case with serum ferritin 4.8 ng/mL and HbA2 3.3% showed a CAP+1 (A>C) mutation.

CONCLUSIONS

Iron deficiency was prevalent among north Indian βTT individuals, especially women. After adjusting for other causes of low HbA2 in βTT, iron deficiency, even when very severe, was very unlikely to interfere significantly with HbA2-based identification of βTT.