Investigation of Osteoprotegerin Gene polymorphisms and Serum level as a predictive marker of cardiac abnormalities in beta thalassemia major

Investigation of Osteoprotegerin Gene polymorphisms and Serum level as a predictive marker of cardiac abnormalities in beta thalassemia major | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigation of Osteoprotegerin Gene polymorphisms and Serum level as a predictive marker of cardiac abnormalities in beta thalassemia major Elham Pourrahim, Zahra Badiei, Hassan Mottaghi, Moghaddam Shahri, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7024553/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Osteoprotegrin is a protein which Prevents excessive bone resorption by binding to the relevant receptor and its genetic variations may contribute to the development of heart disorders. Materials and Methods Eighty-two beta-thalassemia major patients of Sorour medical center of Mashhad, Iran who were older than 11 years old enrolled for the study. Two dimensional and M-mode echocardiography analysis were done in all patients. Sequencing for OPG rs2073617 (950T > C), rs2073618 (1181G > C)] polymorphisms was done using the Sanger method. Serum OPG levels estimated by ELISA. Results Mean age of patients was 23.62 ± 6.92 years. LVH and diastolic dysfunction was present in 34 (41.5%) and 36 (43.9%) patients, respectively. Thalassemia patients with OPG rs2073617, OPG rs2073618 SNPs were at high risk for LVH and diastolic dysfunction as suggested by high odds ratio of 29% and 31%, respectively. Serum OPG levels also were found significantly higher in thalassemia patients with LVH and diastolic dysfunction (P = 0.001). Conclusions OPG rs2073617, rs2073618 SNPs may predispose LVH and diastolic dysfunction in thalassemia patients. Patients with LVH and diastolic dysfunction showed increased levels of serum OPG. of Hematology and Blood Banking Mashhad University of Medical Sciences Mashhad Iran Figures Figure 1 Figure 2 Introduction Beta thalassemia major is one of the most common inherited diseases that requires continuous blood transfusions. The human body cannot excrete excess iron, and iron deposits in the liver, followed by deposition in the heart and endocrine glands ( 1 ). Although blood transfusions have increased the lifespan of beta thalassemia major patients, in transfusion-dependent beta thalassemia major patients, heart failure remains the leading cause of death ( 2 ). Iran, in the Eastern Mediterranean region, and in Central Asia, is one of the important centers of beta thalassemia outbreak, which has served for centuries as a gateway for human population movement in various parts of Asia and Europe ( 3 ). Given the increasing number of consanguineous marriages, it is estimated that there are more than three million beta thalassemia carriers (4 to 8%) and 20,000 patients ( 4 ). There are now more than 18,000 cases of thalassemia in various provinces across the country. This disease has a prevalence of 0.6% in Khorasan Razavi province ( 5 ). Accumulation of iron in heart tissues, especially in ventricular walls, along with other inflammatory factors is as a factor in the development of left ventricular diastolic dysfunction, followed by right ventricular dilatation and high pulmonary hypertension ( 6 ). Seventy nine percent of patients develop diastolic dysfunction, which is the most common symptom of cardiovascular disease in patients with beta thalassemia major ( 7 ). Due to iron overload, the pathway involved in the development of heart failure is formation of reactive oxygen species (ROS) and the inflammatory axis of receptor activator of nuclear factor kappa-B, Receptor activator of nuclear factor-kappa B ligand (RANKL) and osteoprotegrin (OPG) ( 8 ). Osteoprotegin (OPG) is a protein that prevents excessive bone reabsorption by binding to the RANKL and thus forbids it from binding to the receptor ( 9 ). The pleiotropic effect of the RANK / RANKL / OPG axis on the (cardiac) system causes development of ventricular failure in various ways such as increased degradation and inflammation of the matrix ( 10 ). Previous studies have shown a positive association between different OPG gene polymorphisms and heart disease in different groups of people. A meta-analysis study showed that the rs2073617 T > C (950T > C) and rs2073618 G > C (1181G > C) gene polymorphisms are closely related to cardiovascular disorders (48). The 950 T → C polymorphism is located at 129 bp upstream of the TATA box. The 1181 G → C polymorphism is located upstream of exon 1, where the peptide encodes the OPG protein signal, leading to the replacement of lysine with asparagine ( 11 ). Evaluation of the severity of OPG in human myocardial insufficiency by immunostaining and secretion of OPG from the myocardium and increasing the serum level of OPG, shows that there is a relationship between circulating OPG level and left ventricular function. Also, high level of OPG is independently associated with high volume of left ventricular systolic end (LVES) and lower ejection fraction (EF) in both sexes (male and female) ( 12 ). Serum OPG concentrations are associated with the severity of peripheral artery disease and heart failure, symptomatic carotid stenosis, unstable angina, vulnerable carotid plaques and acute myocardial infarction ( 13 ). Due to the fact that cardiac complication in beta thalassemia major patients is diagnosed late because of delayed onset of clinical symptoms and echocardiographic Diagnostic Error, Molecular genetic methods can be used as a predictor for screening in Children with beta thalassemia major to find genotypes predisposing heart disease in these patients considering the constant occurrence of polymorphisms from birth to the end of life. Also, serological methods can be applied to detect the serum level of these factors In order to take the necessary care measures for these people. The aim of the present study was to investigate serum osteoprotegerin levels in patients with beta thalassemia major. Materials and Methods This study was performed on 82 patients with beta thalassemia major referred to Sorour Medical Center (Thalassemia Patients Center) of Mashhad, who had demographic, MRI * T2 and echocardiography information. A general description and echocardiographic findings of beta thalassemia major patients under study presented in Table 1 and 2 . In this study, a total of 82 patients with beta thalassemia major were evaluated. Among them, 70.53% (n = 44) were male and 30.46% (n = 38) were female. Regarding blood transfusion intervals, the majority of patients (86.6%) received transfusions every three weeks, while 7.5% received them every two weeks and 6.1% at intervals longer than three weeks. In terms of the number of blood units received per transfusion, 59.8% of the patients received two units, 35.4% received one unit, and only 4.9% received more than two units. Genotypic distribution based on OPG950 showed that 51.2% were TT, 24.4% were TC, and 24.2% were CC. For the OPG1181 polymorphism, 54.9% of the patients were GG, 25.6% GC, and 19.5% CC. Evaluation of myocardial iron overload using MRI T2* indicated that 46.3% of the patients had normal iron levels, 18.3% had mild iron overload, 15.9% had moderate overload, and 19.5% had severe iron accumulation. Echocardiographic findings among 82 patients with beta-thalassemia major referred to Sorour Medical Center revealed that 58.5% had normal left ventricular mass index (LVMI), while 41.5% showed an increased LVMI. Left ventricular hypertrophy (LVH) was absent in 58.5% of the patients and present in 41.5%. Additionally, 43.9% of the patients demonstrated diastolic dysfunction, whereas 56.1% had no signs of this abnormality. The sample required for this study included blood containing anticoagulants for molecular analysis as well as non-anticoagulant blood for serum analyzes. 5 ml Blood was taken from beta thalassemia patients in Sorour Medical center with the informed consent. Blood samples were collected and frozen at -20°C to be used at the right time for DNA extraction. Serum samples also frozen at -80°C for use in the ELISA test. DNA extraction was performed using QIAGEN Company kit and the quality and purity of extracted DNA evaluated by Nano drop device manufactured by Biotech Company. Since the two SNPs, rs2073617 T > C (950T > C) and rs2073618 G > C (1181G > C) under study in this project were located close together, a single primer designed to test both of them. Primer blast then performed in NCBI database. The resulting fragment length was 550bp.Table 3 shows primer sequence. Table 1 Primers designed for rs2073617 T > C (950T > C) and rs2073618 G > C (1181G > C) Primer Sequence (5 ' ͢ 3') Length Forward GACAGCAGCCGCCTTGTTC 19 Reverse GGAAGCATGGCATAACTTGAAAGC 24 Polymerase Chain Reaction (PCR) reaction performed to amplify the gene fragment. Thermo cycler used in this project was ABI VERITI 96 well PCR machine. PCR program given to thermo cycler showed in the Table 2 . Table 2 PCR program for amplification of desired gene fragment Program PCR Description Step Temperature Time Initial DNA denaturation 95°C 5 minutes Reaction was performed in 35 cycles Denaturation 95°C 30 second Annealing 56°C 1 minute and 30 seconds Extension 72°C 1 minute and 30 seconds Final extension 72°C 7 minutes To confirm amplification of PCR product, it transferred to 2% Agarose gel and electrophoresis revealed length of the fragment by comparing its position to that of a molecular size marker. Afterwards, the PCR product was sent to the central laboratory of Razavi Hospital for sequencing. Sequencing results matched to the NCBI site reference sequence and CLC Genomic Workbench v 3.6.5 software applied to determine if the observed change was a polymorphism. Osteoprotegrin cytokine measured using the Human Osteoprotegerin (OPG) ELISA Kit from Zellbio GmbH COMPANY (Germany). The collected information was described by central indicators and dispersion, frequency tables, as well as the display of appropriate graphs. The Kolmogorov-Smirnov test was used to ensure the normality of the distribution of quantitative variables. If the data distribution was normal, independent t-test was used to compare the means of quantitative variables in the two groups. Otherwise, non-parametric Mann-Whitney test was used. Also, if the hypotheses are true, one-way analysis of variance test was used to compare the means of quantitative variables in the three groups, and if not the nonparametric Kruskal-Wallis test was used. In addition, the relationship between qualitative variables was measured by Chi-square and Fisher's exact test and the relationship between normal and abnormal quantitative variables was measured by Pearson and Spearman correlation coefficient tests, respectively. Logistic regression analysis used to determine the factors associated with cardiac dysfunction in beta thalassemia major patients. The statistical analyses performed using SPSS statistics version 20.0 at a significant level of 0.05. Hardy-Weinberg equilibrium (HWE) formulas were used to identify all possible genotypes in the populations of this project for a specific trait (left ventricular hypertrophy and diastolic dysfunction). Results The echocardiographic and characteristics parameters of beta thalassemia major group are presented in Tables 1 and 2 . The results of Chi-square test showed that there is a statistically significant relationship between LVMI (P = 0.008), LVH (P = 0.008), diastolic dysfunction (P = 0.001) variables and OPG950 polymorphism genotype. Also, results of Chi-square test showed that there is a statistically significant relationship between LVMI (P = 0.03), LVH (P = 0.009) variables and the OPG1181 polymorphism genotype; However, there is no statistically significant relationship between diastolic dysfunction and this genotype (P = 0.22) (Table 3 ). Table 3 Comparison of frequency distribution of echocardiographic in three genotype groups Variable OPG950 P-value OPG1181 P-value CC, n(%) TC, n(%) TT, n(%) CC, n(%) GC, n(%) GG, n(%) LVM without 6(30.0) 12(60.0) 30(71.40) 0.008 5(31.30) 12(57.10) 31(68.90) 0.03 with 14(70.0) 8(40.0) 12(28.60) 11(68.80) 9(42.90) 14(31.10) Total 20(100) 20(100) 42(100) 16(100) 21(100) 45(100) LVH without 6(30.0) 12(60.0) 30(71.40) 0.008 4(25.0) 13(61.90) 31(68.90) 0.009 with 14(70.0) 8(40.0) 12(28.60) 12(75.0) 8(38.10) 14(31.10) Total 20(100) 20(100) 42(100) 16(100) 21(100) 45(100) Diastolic dysfunction without 6(30.0) 12(60.0) 32(76.20) 0.001 8(50.0) 9(42.90) 29(64.40) 0.22 with 14(70.0) 8(40.0) 10(23.80) 8(50.0) 12(57.10) 16(35.60) Total 20(100) 20(100) 42(100) 16(100) 21(100) 45(100) The results in Table 4 showed that in OPG950 polymorphism, there is a statistically significant difference between the mean of EF (P = 0.02) and PAP (P = 0.03) in the three genotype groups. As well, in OPG1181 polymorphism, there is a statistically significant difference between the mean EF in the three genotype groups (P = 0.01), but the mean of PAP in the three OPG1181 genotype groups is not statistically significant (P = 0.63). Table 4 Comparison of mean EF and PAP in three genotype groups Genotype PAP P-value EF P-value Mean ± SD Mean ± SD OPG950 TT 26.54 ± 3.33 0.02 58.40 ± 8.32 0.03 TC 28.90 ± 3.64 54.80 ± 5.57 CC 31.65 ± 6.01 53.50 ± 7.74 OPG1181 GG 27.88 ± 4.53 0.01 58.26 ± 8.38 0.62 GC 29.23 ± 4.92 53.66 ± 6.68 CC 28.56 ± 4.77 54.37 ± 6.27 According to the results of Fisher's exact test in OPG950 (P = 0.32) and OPG1181 (P = 0.09) polymorphisms, there are no significant relationship between MRIT2 and types of genotype in these two polymorphisms (Table 5 ). Table 5 Comparison of MRIT2 frequency distribution in three genotype groups Variable OPG950 P-value OPG1181 P-value CC, n(%) TC, n(%) TT, n(%) CC, n(%) GC, n(%) GG, n(%) MRIT2 7(35.00) 11(55.00) 20(47.60) 0.32 7(43.80) 13(61.90) 18(40.0) 0.09 Normal 3(15.00) 2(10.00) 10(23.80) 3(18.80) 0(0.00) 12(26.70) Mild 3(15.00) 5(25.00) 5(11.90) 4(25.0) 4(19.0) 5(11.10) Moderate 7(35.00) 2(10.00) 7(16.70) 2(12.50) 4(19.0) 10(22.20) Sever 20(100) 20(100) 42(100) 16(100) 21(100) 45(100) The results of Kruskal-Wallis test on the relationship between genotypes and serum OPG levels showed that in both polymorphisms, the mean serum OPG level was statistically significant between the 3 genotype groups (P < 0.001). The results of independent t-test in Table 6 , showed that the mean serum level between patients with LVH and without was statistically significant (P < 0.001). There is also a significant difference between the mean serum levels in patients with and without LVMI (P < 0.001). In addition, the mean serum levels of patients with and without diastolic dysfunction are statistically significant (P < 0.001). Table 6 Comparison of mean serum level variables at the levels of echocardiographic variables Serum OPG Level Variable Mean ±SD P-value LVH Without 1.64 ± 0.070 < 0.001 With 2.21 ± 0.58 Diastolic dysfunction Without 1.58 ± 0.70 < 0.001 With 2.25 ± 0.53 LVMI Normal 1.62 ± 0.69 < 0.001 Increase 2.24 ± 0.56 According to the results of Spearman correlation coefficient test, there is an inverse and significant relationship between serum level variable and EF (P = 0.001). Also, the results of Spearman correlation coefficient test showed that there is a direct relationship between serum level and PAP and this is statistically significant (P = 0.04) (Table 7 ). Table 7 Investigation of the relationship between serum level variable with EF and PAP Serum OPG Level Variable Correlation coefficient (r) P-value EF -0.35 0.001 PAP 0.2 0.04 Multiple logistic regression model was used to determine the factors associated with LVH. The results showed that by controlling the effect of other variables, body mass index and serum OPG had a significant effect on LVH (P = 0.009); but other variables had no significant effect on LVH (P > 0.05). In OPG950 polymorphisms, the chance of developing LVH in patients with TC and CC genotypes is 1.45 and 4.51 times higher than patients with TT genotypes (P = 0.68 and P = 0.09, respectively). Also in OPG1181 polymorphism, the chance of LVH in patients with GC and CC genotype is 0.20 and 1.51 times compared to patients with GG genotype (P = 0.08 and P = 0.64, respectively). Finally, multiple logistic regression model used to determine the factors associated with diastolic dysfunction. The result showed Body mass index (P = 0.03), ferritin level (P = 0.03), transfusion distance (P = 0.03) (more than three weeks compared to two weeks) and OPG950 and OPG1181 genotypes (P = 0.001) had Significant effect on diastolic dysfunction and only the transfusion interval variable (interval of three weeks compared to two weeks) had no significant effect on it (P = 0.61). In OPG950 polymorphism, the chances of developing diastolic dysfunction in patients with TC and CC genotype are 19.28 and 42.27 times higher than patients with TT genotype (P = 0.02 and P = 0.004, respectively. Also in OPG1181 polymorphism, the chance of developing diastolic dysfunction in patients with GC and CC genotype is 0.02 and 0.007 more than GG genotype (P = 0.01 and P = 0.003, respectively). Discussion Frequent blood transfusions to beta thalassemia major patients has confronted them with the limitations of iron deposit poisoning. Although there have been many advances in the management of blood transfusions as well as iron chelators in recent years, it has still not been able to reduce the problems and damage of iron deposition in major organs of the body, including the heart. Due to iron overload, Cardiac complications are also the most important cause of death in these patients ( 2 , 14 ). Serum OPG is known to inhibit stiffness and reduce vascular elasticity as well as inhibit calcification. But high levels of this cytokine act as an aggravating factor ( 15 ). According to it, serum OPG levels can be considered as a marker of the occurrence and progression of heart complications ( 21 ). Different genotypes of OPG950 T > C and OPG1181 G > C polymorphisms can also predict the occurrence of heart complications. Due to the fact that polymorphisms are fixed from birth, it leads to the hypothesis that they can be good predictors of heart disorders ( 16 ). Singh et al. in 2017 by study of serum OPG levels in 105 patients with beta thalassemia major and its association with heart disorders, found that there are a significant difference between diastolic dysfunction and high serum OPG level (P = 0.006) in thalassemia patients with diastolic dysfunction ( 16 ). In our study, as in the Sing study, we observed a significant association between elevated serum OPG levels and occurrence of diastolic dysfunction. The most common complication of diastolic dysfunction is left ventricular hypertrophy (LVH). In the study of Omland et al. ( 12 ), it was found that high serum levels of OPG were associated with left ventricular hypertrophy, so that Due to the effect of other variables, osteoprotein was significantly associated with each of these left ventricular indices in males (P < 0.05 per person), While in females it was associated with increased left ventricular systolic terminal volume and decreased EF and was not associated with LVH. However, in our study, the age and sex variables could not show a difference in serum OPG levels in both men and women with left ventricular hypertrophy (LVH) and in both groups there was a significant relationship between Increased serum OPG levels and the incidence of LVH; The reason can be considered the difference in the nature of the patients studied in Omland with the patients in this study ( 22 ). Due to myocardial iron deposition and the incidence of heart failure in thalassemia major, magnetic resonance imaging (MRI T * 2) can indirectly measure myocardial iron levels. Elfawal et al. ( 17 ), in their study evaluated MRIT * 2 as a non-invasive method for measuring myocardial iron overload in 80 Egyptian patients with beta thalassemia major and observed that there were 73 cases (91.3%) with normal overload, 5 cases (6.3%) with mild overload, one (1.3%) with moderate overload and also only one (1.3%) with sever overload. As a result, this technique can be used as an accurate monitoring tool to improve treatment outcomes in thalassemia patients. While in our study, no significant relationship was observed by examining the relationship between MRIT * 2 and serum OPG levels on 82 beta thalassemia major patients and due to the lack of study on the relationship between MRIT * 2 and serum OPG levels in thalassemia patients, the reason for this cannot be stated ( 23 ). On the other hand, the human OPG gene (8q24) is encoded by common genetic polymorphisms and functionally associated with osteoporosis and is also a primary predictor of cardiovascular disease ( 18 ). Also, Ohmori ( 19 ) showed a significant association between the CC genotype in the OPG T950C polymorphism and the incidence of CAD (coronary artery disease) as well as ACS (acute coronary syndrome) in Japanese men (Asian race) treated with angiography (P < 0.05). Considering the Asian population of the above study and close to population in our study, here, there was also a significant relationship between CC genotype in OPG T950C polymorphism and the incidence of LVH and diastolic dysfunction in thalassemia patients ( 25 ). Similarly, Shen et al. ( 20 ) studied 1092 Chinese patients with major hypertension (HE) in two groups with LVH and without, found that the OPG 1181 G > C polymorphism in patients with major hypertension had a significant relationship between CC genotype and LVH involvement (P = 0.001); Also, an increase in serum OPG levels was seen in people with LVH. Consistent with this study, we also observed patients with beta thalassemia major, who had polymorphism OPG1181G > C and CC genotype, were more susceptible to LVH and Serum OPG level was also high in these patients with LVH ( 26 ). A study by Soufi et al. ( 11 ) on 468 Caucasian men undergoing CAD (a group of diseases that occur in the heart or arteries) performed by sequencing method to determine the relationship between possible genotypes of OPG1181G > C and OPG950T > C polymorphisms and the severity of CAD risk; Which indicates the association of CC genotype in OPG1181G > C and OPG950T > C polymorphisms with CAD risk in Caucasian men. However, in the present study, we investigated the relationship between the genotype and severity of the risk of LVH and diastolic dysfunction on 82 beta thalassemia major patients in northeastern Iran (Caucasian) by similar technique to the Soufi's study ( 11 ) and we observed a close association of CC genotype in OPG1181G > C and OPG950T > C polymorphisms with heart disorders such as LVH and diastolic dysfunction in line with their studies. This similarity of results may be due to the same population in occurrence of polymorphism. Very few similar research has been performed to investigate association of OPG gene polymorphisms with cardiac complications in patients with beta thalassemia major, which requires further research and studies in different ethnicities and specific screenings. The generality of our findings in other age groups or ethnicities is not clear. The study population is relatively small, and our findings in larger samples need to be confirmed, as well as tested in groups of different ethnic backgrounds. Our study had some limitations. Financial constraints prevented the evaluation of other polymorphisms involved in the occurrence of heart disease. Conclusion According to the results of this study, it can be acknowledged that the study of OPG1181G > C and OPG950T > C polymorphisms as well as serum OPG levels can be used as screening markers for cardiac complications (left ventricular hypertrophy and diastolic dysfunction) In patients with beta thalassemia major. In this way, thalassemia born with C allele in the mentioned polymorphisms that have the highest risk of heart complications should be treated promptly and specifically. It is suggested that in addition to rs2073617 and rs2073618 polymorphisms, a study be performed on rs3134069 polymorphisms or other related polymorphisms in other candidate genes. To measure the effect of these polymorphisms on the occurrence of heart disease and the relationship between polymorphisms at the same time. Declarations Ethics approval and consent to participate All human sample acquisition were approved by the Clinical Research Ethics Committee of the Cancer Molecular Pathology Research Center in Mashhad University of Medical Science. Competing interests The authors declare that they have no conflicts of interest. Financial support None. Author Contribution Authors’ contribution: Study conception and design: Pourrahim E, Mahmoudi E, Seyedmoharrami F, & Ayatollahi H; data collection: Badiei Z & Mottaghi Moghaddam Shahri H; analysis and interpretation of results: Keramati MR & Sheikhi M; draft manuscript preparation: Pourrahim E, Mahmoudi E, Seyedmoharrami F, & Ayatollahi H. All authors approved the final version of the manuscript. Acknowledgement We sincerely thank all those who helped in approving, carrying out and completing this project. References Taher AT, Saliba AN. Iron overload in thalassemia: different organs at different rates. Hematology Am Soc Hematol Educ Program. 2017;2017(1):265–71. Shah FT, Sayani F, Trompeter S, Drasar E, Piga A. Challenges of blood transfusions in β-thalassemia. Blood Reviews. 2019;37:100588. De Sanctis V, Kattamis C, Canatan D, Soliman AT, Elsedfy H, Karimi M, et al. β-Thalassemia Distribution in the Old World: an Ancient Disease Seen from a Historical Standpoint. 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The probability of indicating Osteoprotegrin as a biomarker for osteoporosis in patient with thalassemia major. Iranian Journal of Pediatric Hematology & Oncology. 2021. Marcadet L, Bouredji Z, Argaw A, Frenette J. The roles of RANK/RANKL/OPG in cardiac, skeletal, and smooth muscles in health and disease. Frontiers in Cell and Developmental Biology. 2022;10:903657. Jia P, Wu N, Jia D, Sun Y. Association between osteoprotegerin gene polymorphisms and risk of coronary artery disease: a systematic review and meta-analysis. Balkan Journal of Medical Genetics: BJMG. 2017;20(2):27. Sayed SZ, Abd El-Hafez AH, Abu El-ela MA, Mourad MA, Mousa SO. OPG/RANK/RANKL axis relation to cardiac iron-overload in children with transfusion-dependent thalassemia. Scientific Reports. 2023;13(1):12568. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7024553","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507268461,"identity":"a16e056b-fc5a-43a8-99cb-eb8711bfc9af","order_by":0,"name":"Elham Pourrahim","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Elham","middleName":"","lastName":"Pourrahim","suffix":""},{"id":507268463,"identity":"760502c6-e140-4b64-9207-edea0600ff91","order_by":1,"name":"Zahra Badiei","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Badiei","suffix":""},{"id":507268464,"identity":"7927285d-9814-4630-a389-4ce673c30277","order_by":2,"name":"Hassan Mottaghi","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hassan","middleName":"","lastName":"Mottaghi","suffix":""},{"id":507268465,"identity":"16e0bf08-696c-46fe-b848-27546fcc6cf4","order_by":3,"name":"Moghaddam Shahri","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Moghaddam","middleName":"","lastName":"Shahri","suffix":""},{"id":507268466,"identity":"9584eca8-f4c3-4442-9a33-94e3fcdd701b","order_by":4,"name":"Elham Mahmoudi","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Elham","middleName":"","lastName":"Mahmoudi","suffix":""},{"id":507268468,"identity":"294f06ec-ebb3-401a-9857-26e81d85a800","order_by":5,"name":"Mohammad Reza Keramati","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Reza","lastName":"Keramati","suffix":""},{"id":507268469,"identity":"c9f92f24-fe1c-4330-b0a0-258e60175b29","order_by":6,"name":"Hossein Ayatollahi","email":"data:image/png;base64,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","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Ayatollahi","suffix":""},{"id":507268470,"identity":"4a96b23c-64fe-4c32-8839-df4e60684de6","order_by":7,"name":"Fateme Seyedmoharrami","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fateme","middleName":"","lastName":"Seyedmoharrami","suffix":""},{"id":507268473,"identity":"383c77d9-70ae-4baa-857a-b8a0ea4cc0fe","order_by":8,"name":"Maryam Sheikhi","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Sheikhi","suffix":""}],"badges":[],"createdAt":"2025-07-02 03:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7024553/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7024553/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90542577,"identity":"e27e3b63-1887-4a93-94ad-03e4592143d0","added_by":"auto","created_at":"2025-09-04 00:05:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4738,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of mean serum OPG levels in the genotype of OPG 950 and OPG 1181 polymorphisms\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7024553/v1/56979b7b4890754601c2311c.png"},{"id":90541536,"identity":"41ce9b84-fa91-40d0-98b2-fae005b58e64","added_by":"auto","created_at":"2025-09-03 23:57:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4899,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of mean serum OPG levels in people with and without heart disorder.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7024553/v1/7738bacade43a39b35dcd496.png"},{"id":94474877,"identity":"539a54b8-68f9-410a-a31f-014721234b15","added_by":"auto","created_at":"2025-10-27 15:50:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":627808,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7024553/v1/16dfb71c-91c5-4e94-a65e-ab2921bc8111.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation of Osteoprotegerin Gene polymorphisms and Serum level as a predictive marker of cardiac abnormalities in beta thalassemia major","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBeta thalassemia major is one of the most common inherited diseases that requires continuous blood transfusions. The human body cannot excrete excess iron, and iron deposits in the liver, followed by deposition in the heart and endocrine glands (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although blood transfusions have increased the lifespan of beta thalassemia major patients, in transfusion-dependent beta thalassemia major patients, heart failure remains the leading cause of death (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Iran, in the Eastern Mediterranean region, and in Central Asia, is one of the important centers of beta thalassemia outbreak, which has served for centuries as a gateway for human population movement in various parts of Asia and Europe (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Given the increasing number of consanguineous marriages, it is estimated that there are more than three million beta thalassemia carriers (4 to 8%) and 20,000 patients (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). There are now more than 18,000 cases of thalassemia in various provinces across the country. This disease has a prevalence of 0.6% in Khorasan Razavi province (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Accumulation of iron in heart tissues, especially in ventricular walls, along with other inflammatory factors is as a factor in the development of left ventricular diastolic dysfunction, followed by right ventricular dilatation and high pulmonary hypertension (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Seventy nine percent of patients develop diastolic dysfunction, which is the most common symptom of cardiovascular disease in patients with beta thalassemia major (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Due to iron overload, the pathway involved in the development of heart failure is formation of reactive oxygen species (ROS) and the inflammatory axis of receptor activator of nuclear factor kappa-B, Receptor activator of nuclear factor-kappa B ligand (RANKL) and osteoprotegrin (OPG) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Osteoprotegin (OPG) is a protein that prevents excessive bone reabsorption by binding to the RANKL and thus forbids it from binding to the receptor (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe pleiotropic effect of the RANK / RANKL / OPG axis on the (cardiac) system causes development of ventricular failure in various ways such as increased degradation and inflammation of the matrix (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Previous studies have shown a positive association between different OPG gene polymorphisms and heart disease in different groups of people. A meta-analysis study showed that the rs2073617 T\u0026thinsp;\u0026gt;\u0026thinsp;C (950T\u0026thinsp;\u0026gt;\u0026thinsp;C) and rs2073618 G\u0026thinsp;\u0026gt;\u0026thinsp;C (1181G\u0026thinsp;\u0026gt;\u0026thinsp;C) gene polymorphisms are closely related to cardiovascular disorders (48). The 950 T \u0026rarr; C polymorphism is located at 129 bp upstream of the TATA box. The 1181 G \u0026rarr; C polymorphism is located upstream of exon 1, where the peptide encodes the OPG protein signal, leading to the replacement of lysine with asparagine (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEvaluation of the severity of OPG in human myocardial insufficiency by immunostaining and secretion of OPG from the myocardium and increasing the serum level of OPG, shows that there is a relationship between circulating OPG level and left ventricular function. Also, high level of OPG is independently associated with high volume of left ventricular systolic end (LVES) and lower ejection fraction (EF) in both sexes (male and female) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Serum OPG concentrations are associated with the severity of peripheral artery disease and heart failure, symptomatic carotid stenosis, unstable angina, vulnerable carotid plaques and acute myocardial infarction (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Due to the fact that cardiac complication in beta thalassemia major patients is diagnosed late because of delayed onset of clinical symptoms and echocardiographic Diagnostic Error, Molecular genetic methods can be used as a predictor for screening in Children with beta thalassemia major to find genotypes predisposing heart disease in these patients considering the constant occurrence of polymorphisms from birth to the end of life. Also, serological methods can be applied to detect the serum level of these factors In order to take the necessary care measures for these people. The aim of the present study was to investigate serum osteoprotegerin levels in patients with beta thalassemia major.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis study was performed on 82 patients with beta thalassemia major referred to Sorour Medical Center (Thalassemia Patients Center) of Mashhad, who had demographic, MRI * T2 and echocardiography information. A general description and echocardiographic findings of beta thalassemia major patients under study presented in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eIn this study, a total of 82 patients with beta thalassemia major were evaluated. Among them, 70.53% (n\u0026thinsp;=\u0026thinsp;44) were male and 30.46% (n\u0026thinsp;=\u0026thinsp;38) were female. Regarding blood transfusion intervals, the majority of patients (86.6%) received transfusions every three weeks, while 7.5% received them every two weeks and 6.1% at intervals longer than three weeks. In terms of the number of blood units received per transfusion, 59.8% of the patients received two units, 35.4% received one unit, and only 4.9% received more than two units.\u003c/p\u003e\u003cp\u003eGenotypic distribution based on OPG950 showed that 51.2% were TT, 24.4% were TC, and 24.2% were CC. For the OPG1181 polymorphism, 54.9% of the patients were GG, 25.6% GC, and 19.5% CC. Evaluation of myocardial iron overload using MRI T2* indicated that 46.3% of the patients had normal iron levels, 18.3% had mild iron overload, 15.9% had moderate overload, and 19.5% had severe iron accumulation.\u003c/p\u003e\u003cp\u003eEchocardiographic findings among 82 patients with beta-thalassemia major referred to Sorour Medical Center revealed that 58.5% had normal left ventricular mass index (LVMI), while 41.5% showed an increased LVMI. Left ventricular hypertrophy (LVH) was absent in 58.5% of the patients and present in 41.5%. Additionally, 43.9% of the patients demonstrated diastolic dysfunction, whereas 56.1% had no signs of this abnormality.\u003c/p\u003e\u003cp\u003eThe sample required for this study included blood containing anticoagulants for molecular analysis as well as non-anticoagulant blood for serum analyzes. 5 ml Blood was taken from beta thalassemia patients in Sorour Medical center with the informed consent. Blood samples were collected and frozen at -20\u0026deg;C to be used at the right time for DNA extraction. Serum samples also frozen at -80\u0026deg;C for use in the ELISA test. DNA extraction was performed using QIAGEN Company kit and the quality and purity of extracted DNA evaluated by Nano drop device manufactured by Biotech Company. Since the two SNPs, rs2073617 T\u0026thinsp;\u0026gt;\u0026thinsp;C (950T\u0026thinsp;\u0026gt;\u0026thinsp;C) and rs2073618 G\u0026thinsp;\u0026gt;\u0026thinsp;C (1181G\u0026thinsp;\u0026gt;\u0026thinsp;C) under study in this project were located close together, a single primer designed to test both of them. Primer blast then performed in NCBI database. The resulting fragment length was 550bp.Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows primer sequence.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimers designed for rs2073617 T\u0026thinsp;\u0026gt;\u0026thinsp;C (950T\u0026thinsp;\u0026gt;\u0026thinsp;C) and rs2073618 G\u0026thinsp;\u0026gt;\u0026thinsp;C (1181G\u0026thinsp;\u0026gt;\u0026thinsp;C)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSequence (5\u003csup\u003e' ͢\u003c/sup\u003e 3')\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLength\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGACAGCAGCCGCCTTGTTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGGAAGCATGGCATAACTTGAAAGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePolymerase Chain Reaction (PCR) reaction performed to amplify the gene fragment. Thermo cycler used in this project was ABI VERITI 96 well PCR machine. PCR program given to thermo cycler showed in the Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePCR program for amplification of desired gene fragment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eProgram PCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTemperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTime\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInitial DNA denaturation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 minutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eReaction was performed in 35 cycles\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDenaturation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 second\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnealing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 minute and 30 seconds\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 minute and 30 seconds\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinal extension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 minutes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo confirm amplification of PCR product, it transferred to 2% Agarose gel and electrophoresis revealed length of the fragment by comparing its position to that of a molecular size marker.\u003c/p\u003e\u003cp\u003eAfterwards, the PCR product was sent to the central laboratory of Razavi Hospital for sequencing. Sequencing results matched to the NCBI site reference sequence and CLC Genomic Workbench v 3.6.5 software applied to determine if the observed change was a polymorphism. Osteoprotegrin cytokine measured using the Human Osteoprotegerin (OPG) ELISA Kit from Zellbio GmbH COMPANY (Germany).\u003c/p\u003e\u003cp\u003eThe collected information was described by central indicators and dispersion, frequency tables, as well as the display of appropriate graphs. The Kolmogorov-Smirnov test was used to ensure the normality of the distribution of quantitative variables. If the data distribution was normal, independent t-test was used to compare the means of quantitative variables in the two groups. Otherwise, non-parametric Mann-Whitney test was used. Also, if the hypotheses are true, one-way analysis of variance test was used to compare the means of quantitative variables in the three groups, and if not the nonparametric Kruskal-Wallis test was used. In addition, the relationship between qualitative variables was measured by Chi-square and Fisher's exact test and the relationship between normal and abnormal quantitative variables was measured by Pearson and Spearman correlation coefficient tests, respectively. Logistic regression analysis used to determine the factors associated with cardiac dysfunction in beta thalassemia major patients. The statistical analyses performed using SPSS statistics version 20.0 at a significant level of 0.05. Hardy-Weinberg equilibrium (HWE) formulas were used to identify all possible genotypes in the populations of this project for a specific trait (left ventricular hypertrophy and diastolic dysfunction).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe echocardiographic and characteristics parameters of beta thalassemia major group are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results of Chi-square test showed that there is a statistically significant relationship between LVMI (P\u0026thinsp;=\u0026thinsp;0.008), LVH (P\u0026thinsp;=\u0026thinsp;0.008), diastolic dysfunction (P\u0026thinsp;=\u0026thinsp;0.001) variables and OPG950 polymorphism genotype. Also, results of Chi-square test showed that there is a statistically significant relationship between LVMI (P\u0026thinsp;=\u0026thinsp;0.03), LVH (P\u0026thinsp;=\u0026thinsp;0.009) variables and the OPG1181 polymorphism genotype; However, there is no statistically significant relationship between diastolic dysfunction and this genotype (P\u0026thinsp;=\u0026thinsp;0.22) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of frequency distribution of echocardiographic in three genotype groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c3\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eOPG950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eOPG1181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCC, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTC, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTT, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCC, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGC, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eGG, n(%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eLVM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ewithout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6(30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12(60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30(71.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5(31.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12(57.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e31(68.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ewith\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14(70.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12(28.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11(68.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9(42.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14(31.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e21(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e45(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLVH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ewithout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6(30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12(60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30(71.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4(25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13(61.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e31(68.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ewith\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14(70.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12(28.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12(75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8(38.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14(31.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e21(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e45(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eDiastolic dysfunction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ewithout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6(30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12(60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32(76.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8(50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9(42.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e29(64.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ewith\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14(70.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10(23.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8(50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12(57.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16(35.60)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e21(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e45(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed that in OPG950 polymorphism, there is a statistically significant difference between the mean of EF (P\u0026thinsp;=\u0026thinsp;0.02) and PAP (P\u0026thinsp;=\u0026thinsp;0.03) in the three genotype groups. As well, in OPG1181 polymorphism, there is a statistically significant difference between the mean EF in the three genotype groups (P\u0026thinsp;=\u0026thinsp;0.01), but the mean of PAP in the three OPG1181 genotype groups is not statistically significant (P\u0026thinsp;=\u0026thinsp;0.63).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of mean EF and PAP in three genotype groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eOPG950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.40\u0026thinsp;\u0026plusmn;\u0026thinsp;8.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.90\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.65\u0026thinsp;\u0026plusmn;\u0026thinsp;6.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53.50\u0026thinsp;\u0026plusmn;\u0026thinsp;7.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eOPG1181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.26\u0026thinsp;\u0026plusmn;\u0026thinsp;8.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.23\u0026thinsp;\u0026plusmn;\u0026thinsp;4.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53.66\u0026thinsp;\u0026plusmn;\u0026thinsp;6.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.56\u0026thinsp;\u0026plusmn;\u0026thinsp;4.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.37\u0026thinsp;\u0026plusmn;\u0026thinsp;6.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAccording to the results of Fisher's exact test in OPG950 (P\u0026thinsp;=\u0026thinsp;0.32) and OPG1181 (P\u0026thinsp;=\u0026thinsp;0.09) polymorphisms, there are no significant relationship between MRIT2 and types of genotype in these two polymorphisms (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of MRIT2 frequency distribution in three genotype groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eOPG950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eOPG1181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCC, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTC, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTT, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCC, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGC, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGG, n(%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRIT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7(35.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(55.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20(47.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7(43.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13(61.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18(40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3(15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(10.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10(23.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3(18.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12(26.70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3(15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5(11.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4(25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4(19.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5(11.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7(35.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(10.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7(16.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2(12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4(19.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10(22.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e45(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results of Kruskal-Wallis test on the relationship between genotypes and serum OPG levels showed that in both polymorphisms, the mean serum OPG level was statistically significant between the 3 genotype groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results of independent t-test in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, showed that the mean serum level between patients with LVH and without was statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There is also a significant difference between the mean serum levels in patients with and without LVMI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, the mean serum levels of patients with and without diastolic dysfunction are statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of mean serum level variables at the levels of echocardiographic variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eSerum OPG Level\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean \u0026plusmn;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLVH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWithout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWith\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDiastolic dysfunction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWithout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWith\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLVMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to the results of Spearman correlation coefficient test, there is an inverse and significant relationship between serum level variable and EF (P\u0026thinsp;=\u0026thinsp;0.001). Also, the results of Spearman correlation coefficient test showed that there is a direct relationship between serum level and PAP and this is statistically significant (P\u0026thinsp;=\u0026thinsp;0.04) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInvestigation of the relationship between serum level variable with EF and PAP\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eSerum OPG Level\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorrelation coefficient (r)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMultiple logistic regression model was used to determine the factors associated with LVH. The results showed that by controlling the effect of other variables, body mass index and serum OPG had a significant effect on LVH (P\u0026thinsp;=\u0026thinsp;0.009); but other variables had no significant effect on LVH (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In OPG950 polymorphisms, the chance of developing LVH in patients with TC and CC genotypes is 1.45 and 4.51 times higher than patients with TT genotypes (P\u0026thinsp;=\u0026thinsp;0.68 and P\u0026thinsp;=\u0026thinsp;0.09, respectively). Also in OPG1181 polymorphism, the chance of LVH in patients with GC and CC genotype is 0.20 and 1.51 times compared to patients with GG genotype (P\u0026thinsp;=\u0026thinsp;0.08 and P\u0026thinsp;=\u0026thinsp;0.64, respectively).\u003c/p\u003e\u003cp\u003eFinally, multiple logistic regression model used to determine the factors associated with diastolic dysfunction. The result showed Body mass index (P\u0026thinsp;=\u0026thinsp;0.03), ferritin level (P\u0026thinsp;=\u0026thinsp;0.03), transfusion distance (P\u0026thinsp;=\u0026thinsp;0.03) (more than three weeks compared to two weeks) and OPG950 and OPG1181 genotypes (P\u0026thinsp;=\u0026thinsp;0.001) had Significant effect on diastolic dysfunction and only the transfusion interval variable (interval of three weeks compared to two weeks) had no significant effect on it (P\u0026thinsp;=\u0026thinsp;0.61). In OPG950 polymorphism, the chances of developing diastolic dysfunction in patients with TC and CC genotype are 19.28 and 42.27 times higher than patients with TT genotype (P\u0026thinsp;=\u0026thinsp;0.02 and P\u0026thinsp;=\u0026thinsp;0.004, respectively. Also in OPG1181 polymorphism, the chance of developing diastolic dysfunction in patients with GC and CC genotype is 0.02 and 0.007 more than GG genotype (P\u0026thinsp;=\u0026thinsp;0.01 and P\u0026thinsp;=\u0026thinsp;0.003, respectively).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFrequent blood transfusions to beta thalassemia major patients has confronted them with the limitations of iron deposit poisoning. Although there have been many advances in the management of blood transfusions as well as iron chelators in recent years, it has still not been able to reduce the problems and damage of iron deposition in major organs of the body, including the heart. Due to iron overload, Cardiac complications are also the \u003cem\u003emost\u003c/em\u003e important cause of death in these patients (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Serum OPG is known to inhibit stiffness and reduce vascular elasticity as well as inhibit calcification. But high levels of this cytokine act as an aggravating factor (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). According to it, serum OPG levels can be considered as a marker of the occurrence and progression of heart complications (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Different genotypes of OPG950 T\u0026thinsp;\u0026gt;\u0026thinsp;C and OPG1181 G\u0026thinsp;\u0026gt;\u0026thinsp;C polymorphisms can also predict the occurrence of heart complications. Due to the fact that polymorphisms are fixed from birth, it leads to the hypothesis that they can be good predictors of heart disorders (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSingh et al. in 2017 by study of serum OPG levels in 105 patients with beta thalassemia major and its association with heart disorders, found that there are a significant difference between diastolic dysfunction and high serum OPG level (P\u0026thinsp;=\u0026thinsp;0.006) in thalassemia patients with diastolic dysfunction (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In our study, as in the Sing study, we observed a significant association between elevated serum OPG levels and occurrence of diastolic dysfunction. The most common complication of diastolic dysfunction is left ventricular hypertrophy (LVH). In the study of Omland et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), it was found that high serum levels of OPG were associated with left ventricular hypertrophy, so that Due to the effect of other variables, osteoprotein was significantly associated with each of these left ventricular indices in males (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 per person), While in females it was associated with increased left ventricular systolic terminal volume and decreased EF and was not associated with LVH. However, in our study, the age and sex variables could not show a difference in serum OPG levels in both men and women with left ventricular hypertrophy (LVH) and in both groups there was a significant relationship between Increased serum OPG levels and the incidence of LVH; The reason can be considered the difference in the nature of the patients studied in Omland with the patients in this study (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDue to myocardial iron deposition and the incidence of heart failure in thalassemia major, magnetic resonance imaging (MRI T * 2) can indirectly measure myocardial iron levels. Elfawal et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), in their study evaluated MRIT * 2 as a non-invasive method for measuring myocardial iron overload in 80 Egyptian patients with beta thalassemia major and observed that there were 73 cases (91.3%) with normal overload, 5 cases (6.3%) with mild overload, one (1.3%) with moderate overload and also only one (1.3%) with sever overload. As a result, this technique can be used as an accurate monitoring tool to improve treatment outcomes in thalassemia patients. While in our study, no significant relationship was observed by examining the relationship between MRIT * 2 and serum OPG levels on 82 beta thalassemia major patients and due to the lack of study on the relationship between MRIT * 2 and serum OPG levels in thalassemia patients, the reason for this cannot be stated (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, the human OPG gene (8q24) is encoded by common genetic polymorphisms and functionally associated with osteoporosis and is also a primary predictor of cardiovascular disease (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Also, Ohmori (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) showed a significant association between the CC genotype in the OPG T950C polymorphism and the incidence of CAD (coronary artery disease) as well as ACS (acute coronary syndrome) in Japanese men (Asian race) treated with angiography (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Considering the Asian population of the above study and close to population in our study, here, there was also a significant relationship between CC genotype in OPG T950C polymorphism and the incidence of LVH and diastolic dysfunction in thalassemia patients (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Similarly, Shen et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) studied 1092 Chinese patients with major hypertension (HE) in two groups with LVH and without, found that the OPG 1181 G\u0026thinsp;\u0026gt;\u0026thinsp;C polymorphism in patients with major hypertension had a significant relationship between CC genotype and LVH involvement (P\u0026thinsp;=\u0026thinsp;0.001); Also, an increase in serum OPG levels was seen in people with LVH. Consistent with this study, we also observed patients with beta thalassemia major, who had polymorphism OPG1181G\u0026thinsp;\u0026gt;\u0026thinsp;C and CC genotype, were more susceptible to LVH and Serum OPG level was also high in these patients with LVH (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA study by Soufi et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) on 468 Caucasian men undergoing CAD (a group of diseases that occur in the heart or arteries) performed by sequencing method to determine the relationship between possible genotypes of OPG1181G\u0026thinsp;\u0026gt;\u0026thinsp;C and OPG950T\u0026thinsp;\u0026gt;\u0026thinsp;C polymorphisms and the severity of CAD risk; Which indicates the association of CC genotype in OPG1181G\u0026thinsp;\u0026gt;\u0026thinsp;C and OPG950T\u0026thinsp;\u0026gt;\u0026thinsp;C polymorphisms with CAD risk in Caucasian men. However, in the present study, we investigated the relationship between the genotype and severity of the risk of LVH and diastolic dysfunction on 82 beta thalassemia major patients in northeastern Iran (Caucasian) by similar technique to the Soufi's study (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and we observed a close association of CC genotype in OPG1181G\u0026thinsp;\u0026gt;\u0026thinsp;C and OPG950T\u0026thinsp;\u0026gt;\u0026thinsp;C polymorphisms with heart disorders such as LVH and diastolic dysfunction in line with their studies. This similarity of results may be due to the same population in occurrence of polymorphism.\u003c/p\u003e\u003cp\u003eVery few similar research has been performed to investigate association of OPG gene polymorphisms with cardiac complications in patients with beta thalassemia major, which requires further research and studies in different ethnicities and specific screenings. The generality of our findings in other age groups or ethnicities is not clear. The study population is relatively small, and our findings in larger samples need to be confirmed, as well as tested in groups of different ethnic backgrounds. Our study had some limitations. Financial constraints prevented the evaluation of other polymorphisms involved in the occurrence of heart disease.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAccording to the results of this study, it can be acknowledged that the study of OPG1181G\u0026thinsp;\u0026gt;\u0026thinsp;C and OPG950T\u0026thinsp;\u0026gt;\u0026thinsp;C polymorphisms as well as serum OPG levels can be used as screening markers for cardiac complications (left ventricular hypertrophy and diastolic dysfunction) In patients with beta thalassemia major. In this way, thalassemia born with C allele in the mentioned polymorphisms that have the highest risk of heart complications should be treated promptly and specifically. It is suggested that in addition to rs2073617 and rs2073618 polymorphisms, a study be performed on rs3134069 polymorphisms or other related polymorphisms in other candidate genes. To measure the effect of these polymorphisms on the occurrence of heart disease and the relationship between polymorphisms at the same time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003e All human sample acquisition were approved by the Clinical Research Ethics Committee of the Cancer Molecular Pathology Research Center in Mashhad University of Medical Science.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFinancial support\u003c/strong\u003e\u003cp\u003eNone.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthors\u0026rsquo; contribution: Study conception and design: Pourrahim E, Mahmoudi E, Seyedmoharrami F, \u0026amp; Ayatollahi H; data collection: Badiei Z \u0026amp; Mottaghi Moghaddam Shahri H; analysis and interpretation of results: Keramati MR \u0026amp; Sheikhi M; draft manuscript preparation: Pourrahim E, Mahmoudi E, Seyedmoharrami F, \u0026amp; Ayatollahi H. All authors approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe sincerely thank all those who helped in approving, carrying out and completing this project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTaher AT, Saliba AN. Iron overload in thalassemia: different organs at different rates. Hematology Am Soc Hematol Educ Program. 2017;2017(1):265\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShah FT, Sayani F, Trompeter S, Drasar E, Piga A. Challenges of blood transfusions in β-thalassemia. Blood Reviews. 2019;37:100588.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Sanctis V, Kattamis C, Canatan D, Soliman AT, Elsedfy H, Karimi M, et al. β-Thalassemia Distribution in the Old World: an Ancient Disease Seen from a Historical Standpoint. Mediterr J Hematol Infect Dis. 2017;9(1):e2017018-e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaryami F, Azarkeivan A, Fallah MS, Zeinali S. A Large Cohort Study of Genotype and Phenotype Correlations of Beta- Thalassemia in Iranian Population. Int J Hematol Oncol Stem Cell Res. 2015;9(4):198\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhodaei GH, Farbod N, Zarif B, Nateghi S, Saeidi M. Frequency of Thalassemia in Iran and Khorasan Razavi. International Journal of Pediatrics. 2013;1(1):45\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKremastinos DT, Farmakis D, Aessopos A, Hahalis G, Hamodraka E, Tsiapras D, et al. Beta-thalassemia cardiomyopathy: history, present considerations, and future perspectives. Circulation Heart failure. 2010;3(3):451\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmed S, Saleem M, Modell B, Petrou M. 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Circulation. 2005;111(19):2461\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoufi M, Schoppet M, Sattler AM, Herzum M, Maisch B, Hofbauer LC, et al. Osteoprotegerin gene polymorphisms in men with coronary artery disease. The Journal of Clinical Endocrinology \u0026amp; Metabolism. 2004;89(8):3764\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOmland T, Drazner MH, Ueland T, Abedin M, Murphy SA, Aukrust Pl, et al. Plasma osteoprotegerin levels in the general population: relation to indices of left ventricular structure and function. Hypertension. 2007;49(6):1392\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo C, Hu F, Zhang S, Wang Y, Liu H. Association between osteoprotegerin gene polymorphisms and cardiovascular disease in type 2 diabetic patients. 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Iranian Journal of Pediatric Hematology \u0026amp; Oncology. 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarcadet L, Bouredji Z, Argaw A, Frenette J. The roles of RANK/RANKL/OPG in cardiac, skeletal, and smooth muscles in health and disease. Frontiers in Cell and Developmental Biology. 2022;10:903657.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJia P, Wu N, Jia D, Sun Y. Association between osteoprotegerin gene polymorphisms and risk of coronary artery disease: a systematic review and meta-analysis. Balkan Journal of Medical Genetics: BJMG. 2017;20(2):27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSayed SZ, Abd El-Hafez AH, Abu El-ela MA, Mourad MA, Mousa SO. OPG/RANK/RANKL axis relation to cardiac iron-overload in children with transfusion-dependent thalassemia. Scientific Reports. 2023;13(1):12568.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"of Hematology and Blood Banking, Mashhad University of Medical Sciences, Mashhad, Iran","lastPublishedDoi":"10.21203/rs.3.rs-7024553/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7024553/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eOsteoprotegrin is a protein which Prevents excessive bone resorption by binding to the relevant receptor and its genetic variations may contribute to the development of heart disorders.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e\u003cp\u003eEighty-two beta-thalassemia major patients of Sorour medical center of Mashhad, Iran who were older than 11 years old enrolled for the study. Two dimensional and M-mode echocardiography analysis were done in all patients. Sequencing for OPG rs2073617 (950T\u0026thinsp;\u003cem\u003e\u0026gt;\u003c/em\u003e\u0026thinsp;C), rs2073618 (1181G\u0026thinsp;\u003cem\u003e\u0026gt;\u003c/em\u003e\u0026thinsp;C)] polymorphisms was done using the Sanger method. Serum OPG levels estimated by ELISA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMean age of patients was 23.62\u0026thinsp;\u0026plusmn;\u0026thinsp;6.92 years. LVH and diastolic dysfunction was present in 34 (41.5%) and 36 (43.9%) patients, respectively. Thalassemia patients with OPG rs2073617, OPG rs2073618 SNPs were at high risk for LVH and diastolic dysfunction as suggested by high odds ratio of 29% and 31%, respectively. Serum OPG levels also were found significantly higher in thalassemia patients with LVH and diastolic dysfunction (P\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOPG rs2073617, rs2073618 SNPs may predispose LVH and diastolic dysfunction in thalassemia patients. Patients with LVH and diastolic dysfunction showed increased levels of serum OPG.\u003c/p\u003e","manuscriptTitle":"Investigation of Osteoprotegerin Gene polymorphisms and Serum level as a predictive marker of cardiac abnormalities in beta thalassemia major","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 23:57:02","doi":"10.21203/rs.3.rs-7024553/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"493ff802-6a22-4aa4-9a78-c15289893a14","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T14:37:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 23:57:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7024553","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7024553","identity":"rs-7024553","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}