Modelling the mortality of sickle cell disease in Africa.
Modelling the mortality of sickle cell disease in Africa.
复制标题
对非洲镰状细胞病的死亡率进行建模。
DOI:
10.1016/s2352-3026(21)00268-4
复制
发表时间:
2021
期刊:
影响因子:
--
通讯作者:
Kengne,AndrePascal
中科院分区:
文献类型:
--
作者:
Wonkam,Ambroise;Kengne,AndrePascal
In The Lancet Haematology, Obiageli E Nnodu and colleagues presented the findings of their data analysis estimating the mortality of sickle cell disease in children in Nigeria, 1 the country with the largest sickle cell disease burden. They rightfully mentioned the possible underestimation of available global estimates of sickle cell disease mortality from the 2019 Global Burden of Disease study or WorldPop. Therefore they estimated mortality from sickle cell disease in children aged 6–59 months in Nigeria using a model-estimated analysis of data from the 2018 Nigerian Demographic and Health Survey. The investigators found an expected high child mortality rate of sickle cell disease in Nigeria (estimated national average under-5 mortality for children with sickle cell disease born between 2003 and 2013 was 490 per 1000 live births [95% CI 270–700]). Importantly, the study provides some methodological innovations which could be useful in other African settings, under specific conditions, such as the availability of nationwide minimal information on populations socio-demographic survey, with associated reliable screening data on sickle cell disease. Although the study’s models are attractive, they relied heavily on the assumption of Hardy-Weinberg equilibrium as a critical criterion. Hardy-Weinberg equilibrium is a principle stating that genetic variation in a population, such as sickle mutation (HBB-βS; MIM 603903), will remain constant from one generation to the next in the absence of factors that cause variation. However, screening surveys of newborn babies have shown that there is a major deviation from Hardy-Weinberg equilibrium in sickle cell disease in Africa. 2 This deviation is influenced by differential malaria endemicity, with individuals who are carriers of the HbAS and HbAC genes having a selective survival advantage (prevalence of sickle cell disease might be altered by several factors including genetic modifiers, environmental factors such as malaria endemicity and malnutrition, and socioeconomic factors such as household income). A perfect Hardy-Weinberg equilibrium assumes a complete recessive lethality of HBB-βS (relative fitness= 0), whereas homozygotes (HbSS) actually have a relative fitness of 0· 2 (recessive lethality= 0· 8). 3 Another pre-requisite of the Hardy-Weinberg equilibrium is random mating in the population, which is not the case in some African countries, where the consanguinity rate is estimated between 20–70%, particularly in North Africa4 and Nigeria. 5 High rates of consanguinity should be expected to lead to a much higher frequency of HbS and sickle cell disease than estimated, assuming Hardy-Weinberg equilibrium. Moreover, the assumption that sickle cell disease is purely monogenic is only partly correct and contributes to further departure from Hardy-Weinberg equilibrium. Established genetic factors that influence mortality, such as fetal haemoglobin F (HbF) concentrations, related variants in HbF-modulating loci such as in BCL11A (MIM 606557), and coinheritance of alphathalassaemia (MIM 604131) variants, should also be accounted for. Both HbF concentration and alpha-thalassaemia were shown to be modifiers of childhood mortality in a prospective cohort in Kenya, 6 and a cooperative study in the USA. 7 In a study in Cameroon, 8 the coinheritance of 3· 7 kb α-globin gene deletion was associated with delayed onset of clinical manifestations of sickle cell disease in children and occurred more frequently in individuals with sickle cell disease (around 40% vs 20% in controls), a trend found in several settings in Africa, 8, 9 suggesting that alpha-thalassaemia contributes to improved survival of patients …
DOI:
10.1038/gim.2015.143
发表时间:
2016-03
期刊:
Genetics in medicine : official journal of the American College of Medical Genetics
影响因子:
--
作者:
Piel FB;Adamkiewicz TV;Amendah D;Williams TN;Gupta S;Grosse SD
通讯作者:
Grosse SD
影响因子:
3.7
作者:
Rumaney MB;Ngo Bitoungui VJ;Vorster AA;Ramesar R;Kengne AP;Ngogang J;Wonkam A
通讯作者:
Wonkam A