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RANK/RANKL/OPG axis genes relation to cognitive impairment in children with transfusion-dependent thalassemia: a cross-sectional study

Abstract

Background

RANK/RANKL/OPG axis was implicated in many pathological conditions. The study aimed to assess the relationship between the studied RANK, RANKL, and OPG polymorphisms and alleles and cognitive impairment in children with transfusion-dependent thalassemia (TDT).

Methods

This study included 60 TDT children. Real-time PCR was done for: rs1805034, rs1245811, and rs75404003 polymorphisms for the RANK gene, rs9594782 and rs2277438 polymorphisms for the RANKL gene, and rs207318 polymorphism for the OPG gene. The intelligence quotient (IQ) was assessed using the Wechsler Intelligence Scale for Children-Third Edition.

Results

TDT children had a low average total IQ, verbal IQ, and borderline performance IQ. RANK rs1805034 (C > T) had a significant effect on total IQ (p = 0.03). Its TT polymorphism and the CT polymorphism of RANKL rs9494782 (C > T) had a significantly lower total IQ (p = 0.01 for both). The G allele of the RANKL rs2277438 (G > A) had a significantly lower total IQ (p = 0.02). RANK rs1805034 (C > T) and RANKL rs2277438 (G > A) significantly affected verbal IQ (p = 0.01 and 0.03). TT genotype of RANK rs1805034 (C > T) had significantly lower verbal IQ (p = 0.002). Furthermore, the GG genotype of RANKL rs2277438 (G > A) had a significantly lower verbal and performance IQ than the AA genotype (p = 0.04 and 0.01 respectively), and its G allele had a significantly lower performance IQ than the A allele (p = 0.02).

Conclusion

TDT children had low average total and verbal IQ while their performance IQ was borderline. The RANK/RANKL/OPG pathway affects cognition in TDT children, as some of the studied genes’ polymorphisms and alleles had significant effects on total, verbal, and performance IQ of the studied TDT children.

Peer Review reports

Background

Beta thalassemia syndromes are mostly autosomal recessive disorders characterized by beta-globin chains synthesis genetic deficiency. More than 200 mutations cause thalassemia [1]. Beta thalassemia spectrum varies from severe transfusion-dependent anemia in the homozygous state to mild to moderate non-transfusion-dependent microcytic hypochromic anemia in the heterozygous state [2]. Excess unpaired alpha-globin chains aggregate and damage red cell membranes, leading to premature destruction of erythroid precursors resulting in ineffective erythropoiesis. These events cause anemia with erythroid hyperplasia and extramedullary hematopoiesis [3].

Transfusion-dependent thalassemia (TDT) causes several health problems due to profound anemia and frequent blood transfusions such as splenomegaly, bone disease, growth delay, endocrinal disturbances, and blood transfusion-transmitted infections [4, 5]. In addition to long-term transfusion therapy, complications related to iron overload, such as iron overload cardiomyopathy, account for most deaths in thalassemia patients [6].

Regular blood transfusion and chelation therapy have increased the life expectancy in thalassemia patients. Neurological involvement, as a result, has become more evident with the advancement in the age of thalassemia patients [7, 8]. Although most were subclinical, a broad spectrum of neurological complications has also been reported, such as cognitive impairment and cerebrovascular diseases [9].

Multiple risk factors contributing to cognitive impairment in TDT include anemia, iron overload, chronic hypoxia, asymptomatic brain infarcts, and visual and auditory toxicity of deferoxamine [10,11,12,13,14]. Although there is some controversy on the relation between brain iron overload and cognitive impairment in thalassemia, for instance, Manara et al. in 2019 found no evidence of iron overload in brain tissue except in the choroid plexuses. They concluded that iron overload might not directly cause cognitive impairment in thalassemia. However, they proposed that choroid plexus’ iron overload may cause cognitive impairment indirectly. As neurodegeneration secondary to choroid plexus iron overload produces free radicals in the cerebrospinal fluid or tissues contiguous to regions strictly related to cognition [15].

Tumor necrosis factor (TNF) is a pro-inflammatory cytokine that controls the expression of numerous signaling pathways implicated in the progression of immunological reactions related to the development of various vascular and metabolic diseases [16]. Osteoprotegerin (OPG) is a cytokine of the TNF receptor superfamily.

The receptor activator of nuclear factor-κB (RANK) and RANK ligand (RANKL) are a receptor-ligand pair of the TNF receptor superfamily. The RANK/RANKL/OPG axis has emerged as the critical molecular pathway in bone metabolism [17].

Previous studies have elucidated the crosstalk between endothelial cells and osteoblasts during osteogenesis, thus connecting angiogenesis with osteogenesis. However, the cellular mechanisms involved are mainly unknown, but growing evidence suggests that the RANK/RANKL/OPG triad may play a significant role in vascular calcification and different disease mechanisms. Many studies confirmed the critical role of the RANK/RANKL/OPG axis in pathological angiogenesis and inflammation, in addition to its role in cell survival through vascular endothelial growth and activation of the nuclear factor kappa light chain enhancer of activated B cells (NF-κB) pathway [18].

It had been documented that RANK/RANKL/OPG axis signaling is implicated in CNS functioning and corresponding pathologies, processes of differentiation, and cell death. It was reported that this axis is involved in the differentiation of cells involved in neuroinflammation, predominantly in microglia and in resident macrophages and inflammatory cells migrating across the blood-brain barrier. They act as neuroprotectants after brain damage [19].

This study aimed to assess the relationship between the studied RANK, RANKL, and OPG polymorphisms and alleles and cognitive impairment in children with TDT.

Subjects and methods

Study design and participants

This cross-sectional study was carried out at the Pediatric department, Minia University Children and Maternity Hospital, Faculty of Medicine, Minia University, from September 2019 till May 2021. It included 60 children already diagnosed with transfusion-dependent thalassemia, based on previous hemoglobin electrophoresis and clinical course.

They were recruited from the pediatric hematology outpatient clinic and pediatric hematology in-patient ward. All patients were on a regular blood transfusion program every 2–6 weeks and on deferasirox iron chelation therapy for at least 12 months before participating in the study. Age ranged between 5 and 16 years, and there was no sex predilection. Iron overload in the studied children was assessed by serum ferritin, and liver and cardiac MRI.

Children with mental disorders, history of cerebrovascular accidents, any chronic disease other than TDT, or refused to participate were excluded from the study.

Data collection

Baseline clinical assessment

All included children were subjected to detailed medical history taking and thorough clinical examination with particular emphasis on the history of the age of the first transfusion, transfusion burden/year (ml/kg/year), and history of splenectomy, the average frequency of transfusion, and type and duration of chelation therapy. In addition, the socioeconomic status score was determined for every participant child according to El-Gilany et al. [20], a modification of the old scoring system of Fahmy and El-Sherbini [21].

Liver and cardiac MRI

Liver iron concentration (LIC) in mg/g dry weight and T2* MRI was performed in the Department of Radiology, Minia University Children and Maternity Hospital, using MR Philips ingenia 1.5 Tesla (Philips Medical Systems, Netherlands), as part of regular follow-up of the patients.

Laboratory analysis

The following laboratory investigations were done for all participants: CBC, serum ferritin, liver function tests.

About 6 ml of venous blood were withdrawn from each subject by sterile venipuncture, 2 ml were collected on two sterile vacutainers containing EDTA solutions tubes, this tube was used for CBC assay by an automated cell counter (CelltacES, Nihon Kohden, Germany). The remaining 4 ml were put on serum separator gel tubes then were allowed to clot for 30 minutes at 37 °C before centrifugation for 15 minutes at 3500 rpm. The expressed serum measured serum ferritin using fully automated clinical chemistry auto-analyzer system Konelab 60i (Thermo Electron Incorporation, Finland). The remaining serum was stored at − 20 °C.

Molecular analysis

Real-time PCR was done for the following SNPs: rs1805034, rs1245811, and rs75404003 polymorphisms for the RANK gene, rs9594782 and rs2277438 polymorphisms for the RANKL gene, and rs207318 polymorphism for the OPG gene. It was carried on DT lite 4 Real-Time PCR System (DNA Technology, Russian).

For IQ assessment, all the patients were subjected to IQ assessment by using the Arabic version of the Wechsler Intelligence Scale for Children-Third Edition (WISC-R) [22]. This test assesses children’s intelligence on three scales: total IQ, verbal IQ, and performance IQ. Total-scale IQ is based on ten tests incorporated in the verbal and performance (non-verbal) IQ scales. The administration time was approximately 60–90 minutes.

Verbal IQ is based on information, similarities, arithmetic, comprehension, and digit span. The comprehension subtest is a scale of the student’s social knowledge and the depth of development of morals. Similarities subtest measures logic, abstract thinking, and verbal reasoning, while information is a scale of general knowledge, education, and long-term memory of his experience. Arithmetic and digit span subtests are measures of working, short, and long-term memory. Performance IQ is based on picture completion, coding, picture arrangement, block design, and object assembly. Block design measures analyzing and synthesizing an abstract design and producing the design from colored plastic. Picture completion subtest measures students’ capability of recognizing closely related items. The Object Assembly subtest is a measure of the ability of visualization of item parts of Mazes. The mazes subtest measures perceptual organization, visual-motor coordination, and self-control. The IQ was graded based on the following guidelines: ≥ 130 very superior, 129–120 superior, 119–110 high average, 109–90 average, 89–80 low average, 79–70 borderline, ≤69 extremely low [23].

Statistical data analysis

Data will be coded, entered, and analyzed using SPSS (statistical package for social sciences) version 20. Descriptive statistics were calculated and expressed as mean ± standard deviation (SD) for quantitative data and as number and percent for qualitative data. Analytical statistics were done by using independent sample t-test (comparison of quantitative data between two groups) and by ANOVA and post-hoc test (comparison of quantitative data among more than two groups). Correlation testing was done by using Pearson’s and Spearman’s correlation coefficients. Binary logistic regression and linear regression analyses were performed to detect the associated independent factors affecting IQ and test for confounding factors’ effect on IQ. p-value < 0.05 was considered significant.

Results

In this study, 38 (63.3%) of the studied TDT children were males, their mean age was 13 ± 4.1 years, and 32 (53.3%) of them were a result of a consanguineous marriage. Regarding their socioeconomic status, 26 (43.3%) were considered as very-low socioeconomic status, 20 (33.3%) were of low socioeconomic status, and 14 (23.3%) were of medium socioeconomic status. Furthermore, their mean age of starting blood transfusion was 19.2 ± 9 months, 34 (57%) of them were splenectomized, their mean BMI was 18.3 ± 2.1, and their mean age of starting chelation was 7 ± 4.2 years. Furthermore, their mean pre-transfusion hemoglobin was 5.7 ± 0.54 g%. In addition, their mean serum ferritin was 4282.6 ± 2635 ng/ml, their mean liver iron concentration was 12.2 ± 7.7 mg/g dry weight, and their mean cardiac T2* MRI was 17.1 ± 6.3 ms.

Their mean total IQ was 80 ± 11.5 (low average), and their mean verbal IQ was 83. 7 ± 14.1 (low average), while their mean performance IQ was 77.7 ± 9.4 (borderline). The frequencies of different total, verbal, and performance IQ categories are shown in Fig. 1.

Fig. 1
figure 1

Frequency of different total, verbal, and performance IQ categories among studied TDT children

Frequency distribution of different genotypes of the studied polymorphisms among the TDT children are shown in Fig. 2.

Fig. 2
figure 2

Frequency of the studied polymorphisms genotypes among TDT children

Regarding the relation of the studied genes polymorphisms with total IQ, RANK rs1805034 (C > T) polymorphisms had a significant relation with total IQ (p = 0.03). Moreover, the post hoc test revealed that TDT children with its TT polymorphism had significantly lower total IQ than children with the CT polymorphism (p = 0.01). While RANKL rs9494782 (C > T) polymorphisms did not have a significant relation with total IQ (p = 0.1), however, children having the CT polymorphism of this gene had significantly lower total IQ than children with the CC polymorphism on performing the post hoc test (p = 0.01). On studying the alleles, children with the G allele of the RANKL rs2277438 (G > A) had significantly lower total IQ (p = 0.02), with a significant unstandardized β coefficient of − 5.09 (CI 95%: − 9.8 – − 3.37) (p = 0.02) (Table 1).

Table 1 The relation of the studied polymorphisms and alleles of the RANK/RANKL/OPG axis with the total IQ of TDT children

Regarding the relation of the studied genes polymorphisms with verbal IQ, polymorphisms RANK rs1805034 (C > T) and RANKL rs2277438 (G > A) had significant relation with verbal IQ (p = 0.01 and 0.03). At the same time, the homomutant TT genotype of RANK rs1805034 (C > T) had significantly lower verbal IQ than the CT hetero-mutant genotype (p = 0.002), while the GG genotype of RANKL rs2277438 (G > A) had a significantly lower verbal IQ than the AA homomutant genotype (p = 0.04). The unstandardized β coefficient of RANKL rs2277438 (G > A) polymorphisms was 6.5 (CI 95%: 1.4–11.6) (p = 0.02). However, no alleles showed a significant statistical relation with verbal IQ (Table 2).

Table 2 The relation of the studied polymorphisms and alleles of the RANK/RANKL/OPG axis with the verbal IQ of TDT children

No polymorphism showed a significant relation to the performance IQ. However, the GG genotype of RANKL rs2277438 (G > A) had a significantly lower performance IQ than the AA genotype (p = 0.01). Furthermore, the G allele of the RANKL rs2277438 (G > A) had significantly lower performance IQ than the A allele (p = 0.02), with a significant unstandardized β coefficient − 3.924 (− 7.789 – − 2.318) (p = 0.02) (Table 3).

Table 3 The relation of the studied polymorphisms and alleles of the RANK/RANKL/OPG axis with the performance IQ of TDT children

RANK rs1805034 (C > T) polymorphism showed significant negative correlation with total and verbal IQ, as (r = − 0.33, p = 0.01) and (r = − 0.33, p = 0.009) respectively. While RANKL rs2277438 (G > A) polymorphism showed significant positive correlation with verbal IQ (r = 0.38, p = 0.01). Although its G allele correlation with performance IQ did not reach statistical significance (p > 0.05) (Table 4).

Table 4 Correlations of factors affecting total, verbal, and performance IQ in TDT children

Age and serum ferritin had significant negative correlations with total, verbal, and performance IQ in this study regarding other factors that might affect TDT children’s IQ (Table 4).

Linear regression analyses of the factors significantly correlating with total and verbal IQ are shown in Table 5. RANK rs1805034 (C > T) polymorphism’s standardized 𝛃 coefficient reached statistical significance for predicting changes in total and verbal IQ (p = 0.009 and 0.03 respectively). Whereas RANKL rs2277438 (G > A) polymorphism’s standardized 𝛃 coefficient was borderline significant for predicting changes in verbal IQ (p = 0.06) (Table 5).

Table 5 Multiple linear regression analysis of factors correlating with total and verbal IQ

Discussion

Neurological involvement, such as cognitive impairment and cerebrovascular diseases, has become more evident with improved patient care and increased life expectancy in TDT [7,8,9].

Our study demonstrated that TDT children had low total, verbal, and performance IQ. This deterioration of IQ scores is compatible with Meymandi et al. and Canatan et al. studies. They reported significantly lower verbal IQ subsets, performance IQ subsets, and academic problems in 60% of thalassemia children [24, 25]. Our results also agreed with the Egyptian study done by Raafat et al. in 2015, who found that TDT patients had marked lower performances and full-Scale IQ scores [26]. Cognitive impairment in thalassemia has several risk factors, such as chronic anemia resulting in chronic hypoxia, bone marrow expansion and may be iron overload. These factors cause the production of free radicals in the cerebrospinal fluid and tissues contiguous to regions strictly related to cognition [12, 14, 15, 27].. Moreover, asymptomatic brain infarcts, pain and discomfort related to treatment complications, mood changes, and frequent absences from school may aggravate cognitive impairment [10, 11, 28, 29].

Contradictory to our results, Economou et al., 2006 and Alzaree et al., 2018 reported that TDT children had higher scores on the verbal scale [27, 30]. However, other studies claimed that intelligence decline does not necessarily occur in TDT children, and they are just slightly lower than their healthy counterparts. They attributed that to little caring about the quality of education of those children [24, 31]. The contradictory results can be explained by using different assessment tools, and the extent to which the illness had affected the body and how these patients are supported may differ among different study groups [32, 33].

Our study found that polymorphisms of RANK rs1805034 (C > T) affected total IQ and verbal IQ. Moreover, TDT children with TT polymorphism had significantly lower total and verbal IQ than children with CT polymorphism. Additionally, children with the CT polymorphism of RANKL rs9494782 (C > T) had significantly lower total IQ than those with the CC polymorphism. RANKL rs2277438 (G > A) was significantly affecting verbal IQ. Its AA homomutant form had a significantly higher verbal and performance IQ than the GG genotype. Children with the G allele of the RANKL rs2277438 (G > A) had significantly lower total and performance IQ.

Unfortunately, limited research is available addressing the relation of SNPs in our study with cognition; however, these SNPs had been linked to other pathological conditions. Previous studies found that RANK rs1805034 (C > T) polymorphisms might be involved in cardiovascular disorders, and its minor C allele was protective for diastolic dysfunction and osteoporotic hip fracture [16, 34, 35]. The RANKL SNP rs2277438 has been reported as a factor that contributes to the radiographic progression of Rheumatoid arthritis in the Japanese population [36]. Also, Rhee et al., and Cho et al., showed that SNP srs2277438 and rs9594782 of the RANKL gene influenced vascular calcification and bone metabolism in humans [37, 38].

A meta-analysis was done by Song et al. showed that rs2073618 G > C(1181G > C) polymorphisms of the OPG gene were closely related to cardiovascular disorders [39]. In this study, the GG polymorphism of this SNP had lower total, verbal and performance IQ, but this did not reach statistical significance.

A recent study by Ping-Hsun et al. found a relationship between cognitive impairment and the RANK/RANKL/OPG axis; they reported that serum RANKL levels were positively correlated to the cognitive function tests in hemodialysis patients [40]. Moreover, another study found that enhancing RANKL/RANK signaling in animals by recombinant RANKL significantly reduced ischemic brain infarct volume [41].

Also, serum OPG levels were significantly related to cognition [42], and the OPG SNP T245G was significantly associated with an increased risk of ischemic brain stroke [43].

The effect the RANK/RANKL/OPG axis has on cognition may be attributed to its effect on the circulating endothelial progenitor cells, which play a crucial role in pathological angiogenesis and inflammation [18, 44]. As Moazzami et al. in 2020 reported, a lower number of endothelial progenitor cells is associated with cognitive impairment and impairment of verbal memory functions [45]. In addition to the axis involvement in the differentiation of cells involved in neuroinflammation, predominantly in microglia, and in resident macrophages and inflammatory cells migrating across the blood-brain barrier [19].

Limitations

This study is a single-center study that needs to be incorporated into a multi-center study to determine the results on a broader scale with larger sample size. In addition, other genes involved in RANK/RANKL/OPG pathway should also be studied in the future concerning their effect on cognition in transfusion-dependent thalassemia patients. Another limitation is that we have not compared our results with healthy children or children with non-transfusion-dependent thalassemia of the same age and sex. Nevertheless, the aim of this study was limited to assessing the studied genetic markers in TDT children.

Conclusion

In conclusion, TDT children in this study had low average total and verbal IQ while their performance IQ was borderline. Furthermore, this study showed that RANK rs1805034 affected total and verbal IQ, CT polymorphism of RANKL rs9494782 was associated with lower total IQ, and RANKL rs2277438 affected verbal IQ, and its GG genotype was associated with lower performance IQ. Moreover, the RANKL rs2277438 G allele was associated with lower total and performance IQ. Therefore, the RANK/RANKL/OPG pathway impacts cognition in TDT children, and the above SNPs act as genetic markers for cognition impairment in TDT children.

Availability of data and materials

The datasets analyzed during the current study available from the corresponding author on reasonable request.

Abbreviations

TDT:

Transfusion-dependent thalassemia

RANK:

Receptor activator of nuclear factor-κB

RANKL:

Receptor activator of nuclear factor-κB ligand

OPG:

Osteoprotegerin

SNP:

Single-nucleotide polymorphism

IQ:

Intelligence quotient

References

  1. Cao A, Galanello R. Beta-thalassemia. Genet Med. 2010;12(2):61–76. https://doi.org/10.1097/GIM.0b013e3181cd68ed.

    Article  CAS  PubMed  Google Scholar 

  2. Needs T, Gonzalez-Mosquera LF, Lynch DT. Beta Thalassemia. 2021 Oct 17. In: StatPearls. Treasure Island: StatPearls Publishing; 2022.

    Google Scholar 

  3. Mettananda S, Higgs DR. Molecular basis and genetic modifiers of thalassemia. Hematol Oncol Clin North Am. 2018;32(2):177–91. https://doi.org/10.1016/j.hoc.2017.11.003.

    Article  PubMed  Google Scholar 

  4. Tubman VN, Fung EB, Vogiatzi M, Thompson AA, Rogers ZR, Neufeld EJ, et al. Thalassemia clinical research network. Guidelines for the standard monitoring of patients with thalassemia: report of the thalassemia longitudinal cohort. J Pediatr Hematol Oncol. 2015;37(3):e162–9. https://doi.org/10.1097/MPH.0000000000000307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Shah FT, Sayani F, Trompeter S, Drasar E, Piga A. Challenges of blood transfusions in β-thalassemia. Blood Rev. 2019;37:100588. https://doi.org/10.1016/j.blre.2019.100588.

    Article  PubMed  Google Scholar 

  6. Lekawanvijit S, Chattipakorn N. Iron overload thalassemic cardiomyopathy: iron status assessment and mechanisms of mechanical and electrical disturbance due to iron toxicity. Can J Cardiol. 2009;25(4):213–8. https://doi.org/10.1016/s0828-282x(09)70064-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Metafratzi Z, Argyropoulou MI, Kiortsis DN, Tsampoulas C, Chaliassos N, Efremidis SC. T(2) relaxation rate of basal ganglia and cortex in patients with beta-thalassaemia major. Br J Radiol. 2001;74(881):407–10. https://doi.org/10.1259/bjr.74.881.740407.

    Article  CAS  PubMed  Google Scholar 

  8. Akhlaghpoor S, Ghahari A, Morteza A, Khalilzadeh O, Shakourirad A, Alinaghizadeh MR. Quantitative T2* magnetic resonance imaging for evaluation of iron deposition in the brain of β-thalassemia patients. Clin Neuroradiol. 2012;22(3):211–7. https://doi.org/10.1007/s00062-011-0108-z.

    Article  CAS  PubMed  Google Scholar 

  9. Nemtsas P, Arnaoutoglou M, Perifanis V, Koutsouraki E, Orologas A. Neurological complications of beta-thalassemia. Ann Hematol. 2015;94(8):1261–5. https://doi.org/10.1007/s00277-015-2378-z.

    Article  CAS  PubMed  Google Scholar 

  10. Chen JJ. Relation of academic support from parents, teachers, and peers to Hong Kong adolescents' academic achievement: the mediating role of academic engagement. Genet Soc Gen Psychol Monogr. 2005;131(2):77–127. https://doi.org/10.3200/MONO.131.2.77-127.

    Article  PubMed  Google Scholar 

  11. Berkelhammer LD, Williamson AL, Sanford SD, Dirksen CL, Sharp WG, Margulies AS, et al. Neurocognitive sequelae of pediatric sickle cell disease: a review of the literature. Child Neuropsychol. 2007;13(2):120–31. https://doi.org/10.1080/09297040600800956.

    Article  PubMed  Google Scholar 

  12. Duman O, Arayici S, Fettahoglu C, Eryilmaz N, Ozkaynak S, Yesilipek A, et al. Neurocognitive function in patients with β-thalassemia major. Pediatr Int. 2011;53(4):519–23. https://doi.org/10.1111/j.1442-200X.2010.03279.x.

    Article  PubMed  Google Scholar 

  13. Vichinsky EP, Neumayr LD, Gold JI, Weiner MW, Rule RR, Truran D, et al. Neuropsychological dysfunction and neuroimaging abnormalities in neurologically intact adults with sickle cell anemia. JAMA. 2010;303(18):1823–31. https://doi.org/10.1001/jama.2010.562.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Elalfy MS, Aly RH, Azzam H, Aboelftouh K, Shatla RH, Tarif M, et al. Neurocognitive dysfunction in children with β thalassemia major: psychometric, neurophysiologic and radiologic evaluation. Hematology. 2017;22(10):617–22. https://doi.org/10.1080/10245332.2017.1338212.

    Article  CAS  PubMed  Google Scholar 

  15. Manara R, Ponticorvo S, Tartaglione I, Femina G, Elefante A, Russo C, et al. Brain iron content in systemic iron overload: a beta-thalassemia quantitative MRI study. NeuroImage. 2019;24:102058.

    Article  Google Scholar 

  16. Zheng H, Wang C, He JW, Fu WZ, Zhang ZL. OPG, RANKL, and RANK gene polymorphisms and the bone mineral density response to alendronate therapy in postmenopausal Chinese women with osteoporosis or osteopenia. Pharmacogenet Genomics. 2016;26(1):12–9. https://doi.org/10.1097/FPC.0000000000000181.

    Article  CAS  PubMed  Google Scholar 

  17. Rochette L, Meloux A, Rigal E, Zeller M, Cottin Y, Vergely C. The role of osteoprotegerin in the crosstalk between vessels and bone: its potential utility as a marker of cardiometabolic diseases. Pharmacol Ther. 2018;182:115–32. https://doi.org/10.1016/j.pharmthera.2017.08.015.

    Article  CAS  PubMed  Google Scholar 

  18. Potente M, Carmeliet P. The link between angiogenesis and endothelial metabolism. Annu Rev Physiol. 2017;79:43–66. https://doi.org/10.1146/annurev-physiol-021115-105134.

    Article  CAS  PubMed  Google Scholar 

  19. Glasnović A, O'Mara N, Kovačić N, Grčević D, Gajović S. RANK/RANKL/OPG signaling in the brain: a systematic review of the literature. Front Neurol. 2020;11:590480. https://doi.org/10.3389/fneur.2020.590480.

    Article  PubMed  PubMed Central  Google Scholar 

  20. El-Gilany A, El-Wehady A, El-Wasify M. Updating and validation of the socioeconomic status scale for health research in Egypt. East Mediterr Health J. 2012;18(9):962–8. https://doi.org/10.26719/2012.18.9.962.

    Article  CAS  PubMed  Google Scholar 

  21. Fahmy SI, El Sherbini AF. Determining simple parameters for social classification for Health Research. Bull High Inst Public Health. 1983;8:95–107.

    Google Scholar 

  22. Wechsler D. Wechsler intelligence scale for children. 3rd ed. San Antonio: Psychological Corporation; 1991.

    Google Scholar 

  23. El-Alameey IR, Alzaree F, Shehata MA, Shady MM, Atti MA, El-Khonezy MI. Neurocognitive function and its related potentials in children with beta thalassemia major: an Egyptian study. Open Access Maced J Med Sci. 2019;7(3):322.

    Article  Google Scholar 

  24. Meymandi SH, Seyednezhad-Golkhatmi SH, Meymandi MH. A comparison of intelligence quotient in children with and without β-thalassemia major. Galen Med J. 2015;4(4):132–8.

    Article  Google Scholar 

  25. Canatan D, Ratip S, Kaptan S, Cosan R. Psychosocial burden of beta-thalassaemia major in Antalya, South Turkey. Soc Sci Med. 2003;56(4):815–9. https://doi.org/10.1016/s0277-9536(02)00080-1.

    Article  PubMed  Google Scholar 

  26. Raafat N, El Safy U, Khater N, Hassan T, Hassan B, Siam A, et al. Assessment of cognitive function in children with beta-thalassemia major: a cross-sectional study. J Child Neurol. 2015;30(4):417–22. https://doi.org/10.1177/0883073814550827.

    Article  PubMed  Google Scholar 

  27. Alzaree FA, Shehata MA, El Wakeel MA, El-Alameey IR, AbuShady MM, Helal SI. Adaptive functioning and psychosocial problems in children with Beta thalassemia major. Open Access Maced J Med Sci. 2018;6(12):2337–41.

    Article  Google Scholar 

  28. Hasiloglu ZI, Asik M, Ure E, Ertem F, Apak H, Albayram S. The utility of susceptibility-weighted imaging to evaluate the extent of iron accumulation in the choroid plexus of patients with β-thalassaemia major. Clin Radiol. 2017;72(10):903.e1–7. https://doi.org/10.1016/j.crad.2017.04.008.

    Article  CAS  Google Scholar 

  29. Zafeiriou DI, Economou M, Athanasiou-Metaxa M. Neurological complications in beta-thalassemia. Brain Dev. 2006;28(8):477–81. https://doi.org/10.1016/j.braindev.2006.02.005.

    Article  PubMed  Google Scholar 

  30. Economou M, Zafeiriou DI, Kontopoulos E, Gompakis N, Koussi A, Perifanis V, et al. Neurophysiologic and intellectual evaluation of beta-thalassemia patients. Brain Dev. 2006;28(1):14–8. https://doi.org/10.1016/j.braindev.2005.03.006.

    Article  PubMed  Google Scholar 

  31. Karimi M, Yarmohammadi H, Cappellini MD. Analysis of intelligence quotient in patients with homozygous beta-thalassemia. Saudi Med J. 2006;27(7):982–5.

    PubMed  Google Scholar 

  32. Kaufman JC, Robert J, Sternberg RJ. Creativity, Change: The Magazine of Higher Learning. 2007;39(4):55–60. https://doi.org/10.3200/CHNG.39.4.55-C4.

  33. Qasemzadeh MJ, Pirnia SA, Mohebi S, Ebrahimi SM, Ebrahimi H, Ebrahimi H, et al. Correlation of intelligence quotient (IQ) of children younger than 12 years old with history of preterm birth. Galen Med J. 2013;2(3):120–5.

    Article  Google Scholar 

  34. Zhang YP, Liu YZ, Guo Y, Liu XG, Xu XH, Guo YF, et al. Pathway-based association analyses identified TRAIL pathway for osteoporotic fractures. PLoS One. 2011;6(7):e21835.

    Article  CAS  Google Scholar 

  35. Zupan J, Mencej-Bedrač S, Jurković-Mlakar S, Preželj J, Marc J. Gene–gene interactions in RANK/RANKL/OPG system influence bone mineral density in postmenopausal women. J Steroid Biochem Mol Biol. 2010;118(1–2):102–6.

    Article  CAS  Google Scholar 

  36. Wang CM, Tsai SC, Lin JC, Wu YJ, Wu J, Chen JY. Association of genetic variants of RANK, RANKL, and OPG with ankylosing spondylitis clinical features in Taiwanese. Mediat Inflamm. 2019;2019:8029863.

    Google Scholar 

  37. Rhee EJ, Yun EJ, Oh KW, Park SE, Park CY, Lee WY, et al. The relationship between receptor activator of nuclear factor-κB ligand (RANKL) gene polymorphism and aortic calcification in Korean women. Endocr J. 2010;57(6):541–9.

    Article  CAS  Google Scholar 

  38. Cho IJ, Chang HJ, Park HB, Heo R, Shin S, Shim CY, et al. Aortic calcification is associated with arterial stiffening, left ventricular hypertrophy, and diastolic dysfunction in elderly male patients with hypertension. J Hypertens. 2015;33(8):1633–41.

    Article  CAS  Google Scholar 

  39. Song DH, Zhou PZ, Xiu XL, Zou GH, Sun YX, Song C. Relationships of OPG genetic polymorphisms with susceptibility to cardiovascular disease: a meta-analysis. Med Sci Monit. 2016;22:1223.

    Article  CAS  Google Scholar 

  40. Wu PH, Lin YT, Chen CS, Chiu YW, Tsai JC, Kuo PL, et al. Associations of bone turnover markers with cognitive function in patients undergoing hemodialysis. Dis Markers. 2020;2020:8641749.

    PubMed  PubMed Central  Google Scholar 

  41. Shimamura M, Nakagami H, Osako MK, Kurinami H, Koriyama H, Zhengda P, et al. OPG/RANKL/RANK axis is a critical inflammatory signaling system in ischemic brain in mice. Proc Natl Acad Sci. 2014;111(22):8191–6.

    Article  CAS  Google Scholar 

  42. Ross RD, Shah RC, Leurgans S, Bottiglieri T, Wilson RS, Sumner DR. Circulating Dkk1 and TRAIL are associated with cognitive decline in community-dwelling, older adults with cognitive concerns. J Gerontol Ser A Biol Med Sci. 2018;73(12):1688.

    Article  CAS  Google Scholar 

  43. Wu J, Li X, Gao F, Gao S, Lyu J, Qiang H. Osteoprotegerin SNP associations with coronary artery disease and ischemic stroke risk: a meta-analysis. Biosci Rep. 2020;40(10):BSR20202156.

    Article  CAS  Google Scholar 

  44. Lee J, Lee S, Lee CY, Seo HH, Shin S, Choi JW, et al. Adipose-derived stem cell-released osteoprotegerin protects cardiomyocytes from reactive oxygen species-induced cell death. Stem Cell Res Ther. 2017;8(1):1–6.

    Article  Google Scholar 

  45. Moazzami K, Wittbrodt MT, Lima BB, Kim JH, Hammadah M, Ko YA, et al. Circulating progenitor cells and cognitive impairment in men and women with coronary artery disease. J Alzheimers Dis. 2020;74(2):659–68.

    Article  CAS  Google Scholar 

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Acknowledgements

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Financial disclosure

The authors declare no external financial funding.

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Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). No external funding.

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Contributions

SZS, SOM and MAA participated in the design and planning of the study. MAA has done all the lab work. RNS has done the IQ tests for the patients. MAM, SOM and AHA were responsible for recruiting the cases. SOM and AHA participated in data collection, analysis of results and preparation of drafts of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Suzan Omar Mousa.

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The study was explained in details to the parents or legal guardians of the participant children and written informed consents were taken from them. The study was designed respecting the expected ethical aspects. It was performed according to the Declaration of Helsinki 1975, as revised in 2008 and approved by the Institutional Review Board and Medical Ethics Committee of Minia University.

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Mousa, S.O., Abd El-Hafez, A.H., Abu El-ela, M.A. et al. RANK/RANKL/OPG axis genes relation to cognitive impairment in children with transfusion-dependent thalassemia: a cross-sectional study. BMC Pediatr 22, 435 (2022). https://doi.org/10.1186/s12887-022-03479-9

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