Abstract
BackgroundRenal dysfunctions are associated with increased morbidity and mortality in sickle cell disease (SCD). Early detection and subsequent management of SCD patients at risk for renal failure and dysfunctions are essential, however, predictors that can identify patients at risk of developing renal dysfunction are not fully understood.MethodsIn this study, we have investigated the association of 31 known kidney dysfunctions-related variants detected in African Americans from multi-ethnic genome wide studies (GWAS) meta-analysis, to kidney-dysfunctions in a group of 413 Cameroonian patients with SCD. Systems level bioinformatics analyses were performed, employing protein-protein interaction networks to further interrogate the putative associations.ResultsUp to 61% of these patients had micro-albuminuria, 2.4% proteinuria, 71% glomerular hyperfiltration, and 5.9% had renal failure. Six variants are significantly associated with the two quantifiable phenotypes of kidney dysfunction (eGFR and crude-albuminuria): A1CF-rs10994860 (P = 0.02020), SYPL2-rs12136063 (P = 0.04208), and APOL1 (G1)-rs73885319 (P = 0.04610) are associated with eGFR; and WNT7A-rs6795744 (P = 0.03730), TMEM60-rs6465825 (P = 0.02340), and APOL1 (G2)-rs71785313 (P = 0.03803) observed to be protective against micro-albuminuria. We identified a protein-protein interaction sub-network containing three of these gene variants: APOL1, SYPL2, and WNT7A, connected to the Nuclear factor NF-kappa-B p105 subunit (NFKB1), revealed to be essential and might indirectly influence extreme phenotypes. Interestingly, clinical variables, including body mass index (BMI), systolic blood pressure, vaso-occlusive crisis (VOC), and haemoglobin (Hb), explain better the kidney phenotypic variations in this SCD population.ConclusionThis study highlights a strong contribution of haematological indices (Hb level), anthropometric variables (BMI, blood pressure), and clinical events (i.e., vaso-occlusive crisis) to kidney dysfunctions in SCD, rather than known genetic factors. Only 6/31 characterised gene-variants are associated with kidney dysfunction phenotypes in SCD samples from Cameroon. The data reveal and emphasise the urgent need to extend GWAS studies in populations of African ancestries living in Africa, and particularly for kidney dysfunctions in SCD.
Highlights
Sickle Cell Disease (SCD) is a monogenic disease with high prevalence and high mortality rates in Africa
We investigated the associations of these 26 single nucleotide polymorphisms (SNPs) in addition to four previously characterised kidney dysfunction-related variants, including Apolipoprotein L1 (APOL1) (G1 or G2) for rs60910145, rs73885319 and rs71785313, and Heme oxygenase 1 (HMOX1) for rs3074372 and rs743811, relevant to populations of African ancestry (Pattaro et al, 2016), e.g., SCD patients from Cameroon
This prevalence is much higher than the values of 18.5 and 27% observed in paediatric cohorts from several sub-Saharan African countries (Ranque et al, 2014; Aloni et al, 2017), the 13.2% in the multicentric study of children with SCD in the United States (Schaefer et al, 2016) and the 44% in adults from Nigerian and the United States (Bolarinwa et al, 2012; Drawz et al, 2016)
Summary
Sickle Cell Disease (SCD) is a monogenic disease with high prevalence and high mortality rates in Africa. Renal failure caused by recurrent episodes of ischemiareperfusion injury and haemolytic anaemia, occurs in 5–18% of SCD patients and is associated with an increased risk of early mortality (Platt et al, 1994; Gladwin, 2017). The prevention of renal failure relies on early detection and management of kidney dysfunction. In SCD patients, renal failure can be caused by gradual infiltration of glomerulus, which leads to glomerular sclerosis or promotes progression of microalbuminuria to macro-albuminuria/proteinuria and to nephrotic-range proteinuria (Nath and Hebbel, 2015). Renal dysfunctions are associated with increased morbidity and mortality in sickle cell disease (SCD). Detection and subsequent management of SCD patients at risk for renal failure and dysfunctions are essential, predictors that can identify patients at risk of developing renal dysfunction are not fully understood
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