Literature DB >> 22345979

Is RBC discrimination index suitable for differentiating between α- and β- thalassemias?

Ajit C Gorakshakar1, Roshan B Colah.   

Abstract

Entities:  

Year:  2011        PMID: 22345979      PMCID: PMC3276976          DOI: 10.4103/0971-6866.92079

Source DB:  PubMed          Journal:  Indian J Hum Genet        ISSN: 1998-362X


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Hemoglobinopathies impose a significant burden on global healthcare. Approximately 5–7% of the global population carries a potentially pathological hemoglobin gene. This mainly includes the structural hemoglobin variants and different forms of thalassemias. Among the thalassemias, β-thalassemia is very common and is mainly seen in Greece, Cyprus, Italy, the Middle East, and the Indian subcontinent.[1] Detection of β-thalassemia traits (BTTs) or carriers is one of the important steps in the control program for thalassemia. A battery of tests like peripheral blood smear examination, RBC indices, and estimation of HbA2 level are used to identify a carrier. It is known that RBCs from a β-thalassemia carrier are microcytic and hypochromic. Iron deficiency anemia (IDA) is the most common nutrient disorder in the world.[2] This entity also shows microcytic hypochromic RBCs. RBC indices are part of a complete blood count and are used to diagnose the cause of anemia. The mechanism by which anemia occurs alters the RBC indices in a predictable manner. Mean corpuscular volume (MCV) is considered as the most informative parameter and anemias can be categorized as microcytic (MCV <78 fl), normocytic (MCV 80–90 fl), and macrocytic (MCV >100 fl). Both β-thalassemia carriers and IDA cause microcytic anemia. Therefore, several attempts have been made to differentiate between these two conditions by using RBC indices. In the last four decades, several formulae have been designed for this purpose.[3-6] However, none of the formulae has been found to be 100% sensitive or specific for differentiating these two entities. Recently, Niazi et al. (2010)[7] analyzed 312 patients (213 BTT and 89 non-BTT) from Pakistan by calculating seven discrimination indices and found that none of them showed 100% sensitivity and specificity. The percentage of correctly diagnosed cases was the highest for Red Cell Distribution Width Index (RDWI) (84.19%) followed by Mentzer Index (86.85%). It is interesting to note that Jiminez (1993)[8] conducted similar analysis and found that the sensitivity and specificity were only 86.9 and 80.1%, respectively; however, when serum iron concentration was included along with RBC indices to differentiate between BTT and IDA, the sensitivity and specificity rose to 94.2 and 91.6%, respectively, indicating the necessity of some additional test along with RBC indices for differentiation between BTT and IDA. In the α-thalassemias, the most common mutations are of the deletional type. It has been observed that these mutations also decrease the size of RBCs and alter other RBC indices like in β-thalassemia.[910] However, effective screening tests have not been developed so far and DNA analyses remain the only option for accurate diagnosis. Nonetheless, attempts have been made to evaluate various RBC indices for screening α-thalassemia. Sirichrotiyakul et al. (2009)[11] evaluated the use of the osmotic fragility test and MCV to screen for α-thal 1 and / or β-thal in pregnant women from Thailand and found more than 90% sensitivity for both the tests. Pranapanus et al. (2009)[12] considered Hb content and not the size of RBCs for screening α-thal (SEA type) and BTT in pregnant ladies from Thailand. They considered mean corpuscular hemoglobin (MCH) as the best tool for screening and considered MCH <26.5 pg as the best cut-off level for predicating thalassemia carriers. This MCH level gave a sensitivity of 95.2%. Tayapiwatana et al. (2009)[13] developed a novel strategy for screening of α-Thal 1 carriers in Thailand. A sandwich type immunochromatographic strip was developed using monoclonal anti-Bart's antibody. This strip, along with RBC indices, was found to be suitable for screening mass populations for α-thal 1. In the present issue, Mehdi and Al Dahmash (2011)[14] have tried to differentiate between α- and β-thalassemia carriers from Saudi Arabia using RBC indices. The prevalence of α+ thalassemia allele in Peninsular Arabs varies between 0.07 and 0.58 (El Hazmi 1982).[15] The prevalence of β-thalassemia is also high and extremely heterogenous at the molecular level.[16] In both α- and β-thalassemias, varying degree of microcytosis has been observed. In a population like the one from Saudi Arabia, where both α- and β-thalassemias are common along with IDA, there is a possibility that individuals with a combination of any two of the defects are likely to be present. Since the cut-off values for various RBC indices are overlapping in these conditions, the indices alone may not be conclusive to differentiate these entities. In the present study, carriers of α gene deletions had mild microcytosis with or without anemia. The co-existence of α gene deletions generally modifies the phenotype of β-thalassemia patients. In the present study, several cases of BTT as well as of α-thal deletions had normal MCV and MCH values. Thus, the authors concluded that RBC indices only cannot be used to differentiate between α- and β-thalassemias. It could be argued that in this population, using some simple tests like serum iron concentration (Jiminez 1993)[8] or an immunochromatographic strip for screening for α-thalassemia (Tayapiwatana et al. 2009)[13] would be helpful along with RBC indices to differentiate between α- and β-thalassemias. If such a strategy is developed successfully, it would be helpful in the laboratories where hemoglobin electrophoresis is not available.
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Authors:  D R Higgs; D J Weatherall
Journal:  Cell Mol Life Sci       Date:  2009-04       Impact factor: 9.261

2.  Sensitivity and specificity of mean corpuscular hemoglobin (MCH): for screening alpha-thalassemia-1 trait and beta-thalassemia trait.

Authors:  Savitree Pranpanus; Supatra Sirichotiyakul; Kasemsri Srisupundit; Theera Tongsong
Journal:  J Med Assoc Thai       Date:  2009-06

3.  A comparison of the accuracy of the corpuscular fragility and mean corpuscular volume tests for the alpha-thalassemia 1 and beta-thalassemia traits.

Authors:  Supatra Sirichotiyakul; Chanane Wanapirak; Kasemsri Srisupundit; Suchaya Luewan; Theera Tongsong
Journal:  Int J Gynaecol Obstet       Date:  2009-07-09       Impact factor: 3.561

4.  Differentiation of iron deficiency from thalassaemia trait by routine blood-count.

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Journal:  Lancet       Date:  1973-03-03       Impact factor: 79.321

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Authors: 
Journal:  World Health Organ Tech Rep Ser       Date:  1968

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Authors:  B M Ricerca; S Storti; G d'Onofrio; S Mancini; M Vittori; S Campisi; G Mango; B Bizzi
Journal:  Haematologica       Date:  1987 Sep-Oct       Impact factor: 9.941

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Authors:  I Bianco; M P Cappabianca; E Foglietta; M Lerone; G Deidda; L Morlupi; P Grisanti; D Ponzini; S Rinaldi; B Graziani
Journal:  Haematologica       Date:  1997 May-Jun       Impact factor: 9.941

Review 8.  Global burden, distribution and prevention of β-thalassemias and hemoglobin E disorders.

Authors:  Roshan Colah; Ajit Gorakshakar; Anita Nadkarni
Journal:  Expert Rev Hematol       Date:  2010-02       Impact factor: 2.929

9.  Simple method for screening of alpha-thalassaemia 1 carriers.

Authors:  Chatchai Tayapiwatana; Surakit Kuntaruk; Thanusak Tatu; Sawitree Chiampanichayakul; Thongperm Munkongdee; Pranee Winichagoon; Suthat Fuchareon; Watchara Kasinrerk
Journal:  Int J Hematol       Date:  2009-05-14       Impact factor: 2.490

10.  New indices from the H*2 analyser improve differentiation between heterozygous beta or delta beta thalassaemia and iron-deficiency anaemia.

Authors:  C V Jimenez; J Minchinela; J Ros
Journal:  Clin Lab Haematol       Date:  1995-06
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Authors:  Tzu-Chuan Huang; Yi-Ying Wu; Yu-Guang Chen; Shiue-Wei Lai; Sheng-Cheng Wu; Ren-Hua Ye; Chieh-Sheng Lu; Jia-Hong Chen
Journal:  PLoS One       Date:  2015-02-13       Impact factor: 3.240

2.  Associations of anemia and hemoglobin with hemoglobin A1c among non-diabetic workers in Japan.

Authors:  Nobuaki Sakamoto; Huanhuan Hu; Akiko Nanri; Tetsuya Mizoue; Masafumi Eguchi; Takeshi Kochi; Tohru Nakagawa; Toru Honda; Shuichiro Yamamoto; Takayuki Ogasawara; Naoko Sasaki; Akiko Nishihara; Teppei Imai; Toshiaki Miyamoto; Makoto Yamamoto; Hiroko Okazaki; Kentaro Tomita; Akihiko Uehara; Ai Hori; Makiko Shimizu; Taizo Murakami; Keisuke Kuwahara; Ami Fukunaga; Isamu Kabe; Tomofumi Sone; Seitaro Dohi
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