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Serum lipid profile abnormalities among beta-thalassemia patients: a systematic review and meta-analysis

Lipids in Health and Disease volume 23, Article number: 388 (2024) Cite this article

Abstract

Background

Patients with betathalassemia have higher risk of various metabolic disturbances. The literature presents conflicting results about the patterns of abnormal lipid profile among patients with betathalassemia. This systematic review aimed to assess dyslipidemia patterns among patients with betathalassemia when compared with healthy individuals.

Methods

The methods used were adherent to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Systematic searches of the literature were done across Medline/PubMed, Web of Science, Science Direct, and Regional Portal of the World Health Organization Virtual Health Library. Calculation of standardized mean difference (SMD) estimates and their associated 95% confidence intervals (CIs) were done through Jamovi software.

Results

The systematic review included 21 studies meeting the criteria for the analyses. Patients with beta-thalassemia major displayed significantly elevated triglyceride levels (SMD: 0.448, 95% CI, 0.214 to 0.682; P < .001) and reduced total serum cholesterol (SMD: -2.26 (95% CI-2.834 to -1.678; P < .001), as well as decreased levels of both low-density lipoprotein cholesterol (SMD: -1.88, 95% CI, -2.614 to -1.147; P < .001) and high-density lipoprotein cholesterol (SMD: -1.32, 95% CI, -1.786 to -0.860; P < .001). Similarly, beta-thalassemia intermedia patients exhibited comparable lipid profile abnormalities to those with beta-thalassemia major. Conversely, beta-thalassemia minor patients only showed significantly lower total serum cholesterol levels (SMD: -0.66, 95% CI, -0.860 to -0.472; P < .001).

Conclusion

Evidence indicates alterations in lipid profile markers among beta-thalassemia patients. The findings indicate the importance of assessing hypertriglyceridemia and hypocholesterolemia in these patients, especially those with major and intermedia forms, as these lipid profile abnormalities increase the risk of cardiovascular disease.

Background

Beta-thalassemia is a widely occurring hereditary blood condition characterized by a deficiency of the beta-globin chains, which are vital components of hemoglobin structure [1,2,3]. This defect leads to a reduction in functional hemoglobin, resulting in impaired red blood cell formation and chronic anemia [2, 3]. Most thalassemias follow an autosomal recessive inheritance pattern and the various beta-thalassemia forms are broadly categorized into beta-thalassemia major, intermedia, and minor forms [2, 3]. Additionally, other variants of the disorder exist, including HbC/beta-thalassemia and HbS/beta-thalassemia, which involve hemoglobin structure abnormalities. The disease is widespread in tropical and subtropical areas such as the Mediterranean area, Africa, India, and Southeast Asia, with a notable increase in incidence of beta-thalassemia in other areas worldwide [3].

Iron overload is one of the hallmark features of the disorder, and the clinical consequences of iron overload depend on the organs affected by iron deposition, including the pituitary gland, pancreas, thyroid gland, heart, and liver. Iron overload leads to a reduction in antioxidant levels and increased oxidative stress, as well as lipid abnormalities [4,5,6]. Beta-thalassemia is associated with significant oxidative stress. This is because the condition leads to an accumulation of iron in the body and the breakdown of red blood cells, causing cellular damage and various complications [4, 5, 7]. In addition, beta-thalassemia patients have multiple chronic endocrine and metabolic problems [3, 8,9,10,11].

Several studies have investigated abnormalities in the levels of lipid profile markers across the spectrum of beta-thalassemia, consistently reporting impaired lipid indices in affected individuals. Though, there is limited and varied evidence regarding the specific patterns of lipid dysregulation and the potential correlations between lipid profile disturbances and underlying factors or hematological parameters. The current literature lacks a comprehensive meta-analysis to determine whether there are substantial alterations in lipid profile markers between patients with beta-thalassemia and the healthy population. Given that beta-thalassemia is associated with developing metabolic disturbances, it was hypothesized that patients with beta-thalassemia exhibit distinct pattern of dyslipidemia. This study aimed to systematically address this knowledge gap through a meta-analysis, providing new insights into the metabolic complications of beta-thalassemia.

Methods

Search approach

The methods used were adherent to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [12]. Registration of this systematic review was done beforehand on Open Science Framework platform (https://osf.io/4judx). To gather relevant literature, systematic searches of the literature were done across Medline/PubMed, Web of Science, ScienceDirect, and Regional Portal of the World Health Organization Virtual Health Library. The systematic search covered all available literature from its inception up to July 2024. The search included all available relevant studies, regardless of participant demographics or publication date. The publications that were found were uploaded to Endnote software to expedite the process of titles and abstracts screening and remove duplicate entries.

The search plan for the present systematic review involved the following terms: (((“Thalassemia“[Mesh] OR “beta-Thalassemia“[Mesh]) OR (thalassemia[Text Word] OR “beta-thalassemia“[Text Word] OR β-thalassemia[Text Word])) AND ((“Cholesterol“[Mesh] OR “Cholesterol, LDL“[Mesh] OR “Cholesterol, HDL“[Mesh] OR “Hypercholesterolemia“[Mesh]) OR (“lipid profile“[Text Word] OR “cholesterol“[Text Word] OR “triacylglycerol“[Text Word] OR triglyceride[Text Word] OR “high-density lipoprotein“[Text Word] OR “low-density lipoprotein“[Text Word] OR “HDL“[Text Word] OR “LDL“[Text Word] OR “HDL-C“[Text Word] OR “LDL-C"OR “Dyslipidemia“[Text Word] OR “Dyslipidaemia“[Text Word] OR “Hyperlipidemia“[Text Word] OR “Hypolipidemia“[Text Word] OR Hyperlipoproteinemia[Text Word] OR Hypolipoproteinemia[Text Word] OR “Hypercholesterolemia“[Text Word] OR Hypocholesterolemia[Text Word]))).

Inclusion and exclusion criteria

The PICO model was used to frame the research query. The “population” included people with and without beta-thalassemia. The “intervention” was the occurrence of beta-thalassemia. The “comparison” was performed with respect to healthy controls. The assessed “outcomes” were the mean levels of lipid profile indices.

The process of selection involved a two-step method. Firstly, the titles and abstracts of all identified studies were examined to find possibly relevant articles. After that, a comprehensive full-text review of these studies was conducted to assess their eligibility based on the predefined inclusion criteria. These criteria included original human case-control, cross-sectional, or cohort studies that provided explicit data on one or more of the key indicators of the lipid profile, explicitly total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL), and high-density lipoprotein cholesterol (HDL) among patients and healthy participants. The exclusion criteria included review articles, case reports, editorials, abstracts, duplicate publications, studies lacking healthy control groups, and studies that did not provide explicit data on variables of interest.

Quality assessment and data extraction

To assess the methodological thoroughness and potential biases in the included studies, the critical appraisal and assessment checklists prepared by the Joanna Briggs Institute were used (https://jbi.global/critical-appraisal-tools). The checklists expedite the assessment of potential bias in study conduct and data analysis. The data extraction process involved four independent reviewers, who extracted relevant information from each study. Any discrepancies or inconsistencies among the reviewers were set by discussion and agreement. The data extracted from all articles were the following: authors, region, year, number of patients, age group, and mean levels of the key indicators of the serum lipid profile. In addition, any significant associations with lipid profile abnormalities, if reported, were summarized.

Certainty of evidence

The GRADE framework (Grading of Recommendations Assessment, Development and Evaluation) was applied for weighing confidence in the estimates for all outcomes across the included studies. Confidence in the estimates was categorized into four ranks: high, moderate, low, or very low. The assessment began at low certainty of evidence because of the distinctive limitations of observational studies. The evidence certainty of was then downgraded or upgraded by one or two levels based on the GRADE domains. Further details regarding the GRADE domains and the reasons for downgrading or upgrading are described in previous reports.

Data analysis

The analyses were performed utilizing Jamovi software (https://www.jamovi.org) to calculate the pooled standardized mean difference (SMD) estimates and their associated 95% confidence intervals (CIs). The random-effects model (DerSimonian–Laird method) was implemented to account for heterogeneity among the reviewed publications. Heterogeneity among studies was evaluated by utilizing the I² statistic, which quantifies the variation proportion in effect estimates because of the true heterogeneity rather than chance. Publication bias, a potential source of bias because of the overrepresentation of publications with significant results, was assessed utilizing Begg’s and Egger’s tests and inspection of funnel plots [13,14,15]. The Duval and Tweedie trim-and-fill analysis was implemented to address publication bias caused by missing articles [16].

Results

Studies characteristics

Initially, 898 studies were identified through the search. After removing duplicates, 585 studies remained for titles and abstracts screening process. Of these, a total of 547 records were omitted due to irrelevance, leaving 38 for full-text review. After further screening, 17 studies were excluded, resulting in 21 eligible studies for inclusion in the analyses. These 21 studies included data from 1,587 patients with beta-thalassemia and 1,659 healthy individuals. The studies selection process was illustrated in Fig. 1 [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36].

The majority of the indicated that individuals with beta-thalassemia, irrespective of the specific type, tend to exhibit several lipid abnormalities (Table 1). These include hypocholesterolemia, with reduced concentrations of both LDL and HDL, and elevated concentration of TG. The key features of the reviewed publications, as well as risk of bias assessment, are illustrated in Table 1.

Table 1 Baseline characteristics of the studies included in the review
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Lipid profile among patients with beta-thalassemia major

There were 15 studies with explicit data to calculate the SMD of TG and TC estimates. For TC, the pooled effect size showed a significant association between beta-thalassemia major and reduced serum TC levels, with SMD = -3.54 (95% CI, -4.442 to -2.632; P < .001) (Fig. 2). Regarding TG, the pooled effect size showed that that patients with beta-thalassemia major displayed significantly higher serum TG levels than the healthy individuals, with SMD = 1.91 (95% CI, 1.277 to 2.540; P < .001) (Fig. 2).

In addition, there were 13 studies with explicit data to calculate the SMD estimates of LDL and HDL. The analysis showed significantly lower LDL and HDL cholesterol levels in beta-thalassemia major patients compared to healthy people. The pooled SMD of LDL and HDL were − 2.81 (95% CI, -3.654 to -1.959; P < .001) and − 2.75 (95% CI, -3.668 to -1.837; P < .001) (Fig. 2).

Lipid profile among patients with beta-thalassemia intermedia

There were five studies with explicit data to calculate the SMD of TG and TC estimates. Beta-thalassemia intermedia patients exhibited comparable lipid profile abnormalities to those with beta-thalassemia major. The results showed that people with beta-thalassemia intermedia had significantly lower TC concentrations than healthy individuals, while their TG levels were significantly higher. For TC, the pooled SMD = -2.26 (95% CI-2.834 to -1.678; P < .001) (Fig. 3). The analysis of the serum TG levels showed SMD = 0.448 (95% CI, 0.214 to 0.682; P < .001) (Fig. 3).

Additionally, there were six studies with explicit data to calculate the SMD of LDL and HDL estimates. The analysis showed that both of them were significantly lower among beta-thalassemia patients. The pooled SMD of LDL and HDL were − 1.88 (95% CI, -2.614 to -1.147; P < .001) and − 1.32 (95% CI, -1.786 to -0.860; P < .001) (Fig. 3).

Lipid profile among patients with beta-thalassemia minor

There were three studies and five studies with explicit data to calculate the SMD of TC and TG estimates, respectively. For TC, the pooled effect size showed that the serum TC concentrations in beta-thalassemia minor patients were significantly lower than their controls, with SMD of -0.66 (95% CI, -0.860 to -0.472; P < .001) (Fig. 4). In contrast to beta-thalassemia major and beta-thalassemia intermedia, no substantial difference noted between the groups regarding TG levels, with SMD = 0.08 (95% CI, − 0.051 to 0.215; P = .228). In addition, there were four studies with explicit data to calculate the SMD of LDL and HDL estimates. The analysis revealed that there was no significant variance regarding both of LDL and HDL. The pooled SMD of LDL and HDL were − 0.311 (95% CI, − 0.884 to 0.263; P = .289) and − 0.143 (95% CI, -0.622 to 0.335; P = .556), respectively (Fig. 4).

Publication bias analyses and GRADE assessment

Potential publication bias was assessed by examining funnel plots (Supplementary Files 1–3, Figs. S1-S3) and conducting Begg’s and Egger’s tests (Table 2). The publication bias tests were significant mainly for results of the beta-thalassemia major analyses. To further investigate and adjust for any potentially missing articles, the Duval and Tweedie trim-and-fill analysis was utilized. However, this method did not detect any absent studies, indicating that the adjusted estimates remain consistent with the original findings across all analyses.

The quality of evidence in the GRADE assessment was judged to be low for all results related to dyslipidemia in patients with beta-thalassemia major and moderate for most results of dyslipidemia among patients with beta-thalassemia intermedia. Regarding beta-thalassemia minor, the evidence quality was assessed as moderate for the results of TC and TG and low for the results of LDL and HDL. The most common reasons for downgrading the certainty of evidence were inconsistency across included studies and publication bias. The summary of the GRADE evidence profile is presented in Supplementary File 4, Table S1.

Fig. 1
figure 1

Schematic of study selection

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Table 2 Results of publication bias and heterogeneity
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Fig. 2
figure 2

Forest plot illustrating pooled SMD estimates for the analyses of beta-thalassemia major

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Fig. 3
figure 3

Forest plot illustrating pooled SMD estimates for the analyses of beta-thalassemia intermedia

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Fig. 4
figure 4

Forest plot illustrating pooled SMD estimates for the analyses of beta-thalassemia minor

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Discussion

This systematic review revealed noteworthy alterations in the markers of lipid profiles of patients with beta-thalassemia. These alterations were observed consistently across beta-thalassemia major and intermedia, with low serum levels of TC, HDL, and LDL levels and high serum levels of TG. Likewise, this pattern of dyslipidemia (low TC, LDL, HDL, and high TG) was reported among patients with sickle cell disease [37,38,39,40].

The exact pathogenesis of hypertriglyceridemia and hypocholesterolemia in beta-thalassemia population is not fully understood. However, there are several characteristics of the disease that could contribute to the pathogenesis, such as oxidative stress and liver damage secondary to excessive iron overload in beta-thalassemia, as well as insulin resistance that can disrupts the normal regulation of fat metabolism [35, 41]. The association between ferritin and both hypertriglyceridemia and low HDL cholesterol was described by large, nationwide surveys on the general population from the United States and South Korea [42, 43].

Hypocholesterolemia in beta-thalassemia patients can arise from multiple factors: the high cholesterol requirement due to active or amplified erythropoiesis to counteract lower survival of red blood cells or increased hemolysis; iron overload leading to decreased hepatic biosynthesis of cholesterol; plasma dilution due to anemia; and macrophage system stimulation with cytokine release which affect cholesterol synthesis [18, 19, 40, 44]. Moreover, some studies have showed that beta-thalassemia trait had a modifying and cholesterol-lowering effect among people with familial hypercholesterolemia [44, 45].

A few of the included studies investigated lipid profile abnormalities and their correlation with potential moderating factors. Most of them found that the altered lipid profile indicators in patients with beta-thalassemia were not depending on factors like age, sex, or ferritin levels [20, 22, 24, 27]. In addition, the relationship between lipid profiles and clinical manifestations or hematological parameters of erythropoietic activity, such as reticulocyte counts and hemoglobin levels, was not assessed by most of the included studies. However, a 2006–2018 nationwide population-based study from South Korea found that co-morbidities, including dyslipidemia, were significantly linked with the frequency of blood transfusion in beta-thalassemia patients [3]. On the other hand, studies conducted on patients with sickle cell disease did assess these clinical manifestations. These studies reported a correlation between extent of the lipid profile abnormalities and many of the clinical manifestations constituting a more severe course of the disease [37, 38, 40].

Previous studies found a high risk of cardiovascular complications, including atherosclerosis and heart failure, among individuals living with beta-thalassemia [36, 46, 47]. The finding of low cholesterol profiles in thalassemia patients suggests that the underlying mechanisms for their atherosclerosis and cardiovascular risk extend beyond the conventional understanding of cholesterol’s role and point to the possibility of additional explanatory factors that may contribute to the elevated cardiovascular risk observed in this disorder.

However, the dyslipidemic profile of hypertriglyceridemia and low HDL levels found in this meta-analysis could contribute to the cardiovascular risk [35, 48]. Moreover, a few of the reviewed studies described pro-atherogenic biochemical phenotypes in patients with beta-thalassemia [22, 29, 35]. These studies and other studies indicate that atherogenic indexes, like the Castelli’s risk indexes I (TC/HDL) & II (LDL/HDL), atherogenic index of plasma [log(TG/ HDL)], TC: HDL ratio, and atherogenic coefficient (AC) [(TC - HDL)/HDL], were significantly raised in beta-thalassemia patients, explaining the high cardiovascular risk [48]. In addition, findings of Aggeli et al. of higher sVCAM-1, sICAM-1, and IL-6 levels are suggesting potential involvement of inflammation and endothelial dysfunction [17].

Strengths and limitations

The present systematic review has several strength points. One key strength is the inclusion and separate analysis of all beta-thalassemia subtypes—major, intermedia, and minor. By examining each subtype individually, this systematic review provides detailed insights into the lipid profile characteristics associated with each form of beta-thalassemia. Despite the initial detection of potential publication bias in some analyses, the application of the Duval and Tweedie trim-and-fill method did not detect missing articles, which strengthens the reliability of the results. Additionally, the application of the GRADE framework offered a structured and transparent evaluation of the certainty of evidence, adding credibility to the conclusions drawn from the findings.

Nevertheless, few limitations of this systematic review should be acknowledged. Firstly, the heterogeneity of the studies and the limitations in the available data restrict the ability to conduct further meta-analyses to explore additional aspects of dyslipidemia beyond estimating overall differences. Limited data were available regarding the clinical consequences of abnormal lipid metabolism among patients with beta-thalassemia. Most studies did not assess the impact of moderating factors influencing lipid profile abnormalities, such as disease severity, transfusion regimens, or chelation therapy, representing a knowledge gap that necessitates further research. The search primarily focused on basic lipid profile indices, excluding novel lipid indices such as small dense LDL, apolipoprotein B, and lipoprotein(a). Additionally, the omission of grey literature and the inclusion of only English-language studies could limit the overall representativeness of the findings.

Conclusion

There is evidence indicates noteworthy variations in lipid profile markers among patients with beta-thalassemia. This systematic review demonstrates that both patients with beta-thalassemia major and those with beta-thalassemia intermedia exhibit increased TG levels and decreased HDL levels compared to healthy individuals. The findings highlight the importance assessing hypertriglyceridemia and hypocholesterolemia in these patients, especially those with major and intermedia forms, particularly those with major and intermedia forms, as these abnormalities increase the risk of cardiovascular disease.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

TG:

Triglyceride

TC:

Total cholesterol

HDL:

High density lipoprotein

LDL:

Low density lipoprotein

References

  1. Goldberg EK, Lal A, Fung EB. Nutrition in Thalassemia: a systematic review of Deficiency, relations to morbidity, and supplementation recommendations. J Pediatr Hematol Oncol. 2022;44(1):1–11.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Sanchez-Villalobos M, Blanquer M, Moraleda JM, Salido EJ, Perez-Oliva AB. New insights into Pathophysiology of β-Thalassemia. Front Med. 2022;9:880752.

    Article  Google Scholar 

  3. Lee JS, Rhee TM, Jeon K, Cho Y, Lee SW, Han KD, et al. Epidemiologic trends of Thalassemia, 2006–2018: a Nationwide Population-based study. J Clin Med. 2022;11(9):2289.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Sharifi-Zahabi E, Abdollahzad H, Mostafa Nachvak S, Moloudi J, Golpayegani MR, Asiaei S et al. Effects of alpha lipoic acid on iron overload, lipid profile and oxidative stress indices in β‐thalassemia major patients: a cross‐over randomised controlled clinical trial. Int J Clin Pract. 2021;75(6).

  5. Margekar S, Sud R, Aggarwal R. Dyslipidemia in adult thalassemia patients. Indian J Med Spec. 2021;12(1):44.

    Article  Google Scholar 

  6. Bou-Fakhredin R, De Franceschi L, Motta I, Eid AA, Taher AT, Cappellini MD. Redox Balance in β-Thalassemia and sickle cell disease: a love and hate relationship. Antioxidants. 2022;13(5):967.

    Article  Google Scholar 

  7. Neaimy KSA, Alkhyatt MM, Jarjess IA. New insights of oxidative stress and Thalassemia May lead to antioxidant therapy. Pharmacogn J. 2024;28(1):202–4.

    Article  Google Scholar 

  8. Kumar T, Basu S, Kundu R, Majumdar I, Mukherjee D. Lipid Profile in Children with Thalassemia: a prospective Observational Study from Eastern India. Indian Pediatr. 2020;57(11):1072–3.

    Article  PubMed  Google Scholar 

  9. Hokland P, Daar S, Khair W, Sheth S, Taher AT, Torti L, et al. Thalassaemia—A global view. Br J Haematol. 2023;201(2):199–214.

    Article  PubMed  Google Scholar 

  10. Al-Akhras A, Badr M, El-Safy U, Kohne E, Hassan T, Abdelrahman H, et al. Impact of genotype on endocrinal complications in β-thalassemia patients. Biomed Rep. 2016;4(6):728–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Nasir C, Rosdiana N, Lubis AD. Correlation between 25-Hydroxyvitamin D and lipid Profile among children with Beta thalassemia Major. Open Access Maced J Med Sci. 2018;10(10):1790–4.

    Article  Google Scholar 

  12. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol. 2009;62(10):e1–34.

    Article  PubMed  Google Scholar 

  13. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for Publication Bias. Biometrics. 1994;50(4):1088.

    Article  CAS  PubMed  Google Scholar 

  14. Wallace BC, Dahabreh IJ, Trikalinos TA, Lau J, Trow P, Schmid CH. Closing the Gap between Methodologists and End-Users: R as a Computational Back-End. J Stat Softw. 2012;49(5). http://www.jstatsoft.org/v49/i05/

  15. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods. 2010;1(2):97–111.

    Article  PubMed  Google Scholar 

  16. Duval S, Tweedie R. Trim and fill: a simple funnel-plot–based method of testing and adjusting for Publication Bias in Meta‐Analysis. Biometrics. 2000;56(2):455–63.

    Article  CAS  PubMed  Google Scholar 

  17. Aggeli C, Antoniades C, Cosma C, Chrysohoou C, Tousoulis D, Ladis V, et al. Endothelial dysfunction and inflammatory process in transfusion-dependent patients with beta-thalassemia major. Int J Cardiol. 2005;105(1):80–4.

    Article  PubMed  Google Scholar 

  18. Ahmad Ibrahim O, Ahmad AB, Nigm DA, Hussien AN, Mohammad Ibrahim WH. Subclinical atherosclerotic predictive value of inflammatory markers in thalassemia intermedia patients. Expert Rev Hematol. 2021; 3;14(7):669–77.

  19. Al-Quobaili FA, Abou Asali IE. Serum levels of lipids and lipoproteins in Syrian patients with beta-thalassemia major. Saudi Med J. 2004;25(7):871–5.

    PubMed  Google Scholar 

  20. Amendola G, Danise P, Todisco N, D’Urzo G, Di Palma A, Di Concilio R. Lipid profile in β -thalassemia intermedia patients: correlation with erythroid bone marrow activity. Int J Lab Hematol. 2007;29(3):172–6.

    Article  CAS  PubMed  Google Scholar 

  21. Ayyash H, Sirdah M. Hematological and biochemical evaluation of β-thalassemia major (βTM) patients in Gaza Strip: a cross-sectional study. Int J Health Sci. 2018;12(6).

  22. Daswani P, Garg K. Lipid profile in ?-thalassemia major children and its correlation with various parameters. Indian J Child Health. 2021;8(1):26–31.

    Article  Google Scholar 

  23. Ghorban K, Shanaki M, Mobarra N, Azad M, Asadi J, Pakzad R, et al. Apolipoproteins A1, B, and other prognostic biochemical cardiovascular risk factors in patients with beta-thalassemia major. Hematology. 2016;21(2):113–20.

    Article  CAS  PubMed  Google Scholar 

  24. Goldfarb AW, Rachmilewitz EA, Eisenberg S. Abnormal low and high density lipoproteins in homozygous beta-thalassaemia. Br J Haematol. 1991;79(3):481–6.

    Article  CAS  PubMed  Google Scholar 

  25. Gozashti MH, Hasanzadeh A, Mashrouteh M. Prevalence of metabolic syndrome in patients with minor beta thalassemia and its related factors: a cross-sectional study. J Diabetes Metab Disord. 2014;13(1):108.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Haghpanah S, Davani M, Samadi B, Ashrafi A, Karimi M. Serum lipid profiles in patients with beta-thalassemia major and Intermedia in southern Iran. J Res Med Sci. 2010;15(3):150–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Hartman C, Tamary H, Tamir A, Shabad E, Levine C, Koren A, et al. Hypocholesterolemia in children and adolescents with β-thalassemia intermedia. J Pediatr. 2002;141(4):543–7.

    Article  PubMed  Google Scholar 

  28. Hashemieh M, Javadzadeh M, Shirkavand A, Sheibani K. Lipid profile in minor thalassemic patients: a historical cohort study. Bangladesh Med Res Counc Bull. 2011;37(1):24–7.

    Article  CAS  PubMed  Google Scholar 

  29. Jabbar HK, Hassan MK, Al-Naama LM. Lipids profile in children and adolescents with β-thalassemia major. Hematol Transfus Cell Ther. 2023;45(4):467–72.

    Article  PubMed  Google Scholar 

  30. Keşkek ŞÖ, Demirtaş D, Uysal G, Başaran E. Sexual dysfunction in female subjects with beta-thalassemia minor. Int J Impot Res. 2020;32(3):358–62.

    Article  PubMed  Google Scholar 

  31. Kırım S, Keşkek ŞÖ, Turhan A, Saler T. Is β-Thalassaemia minor Associated with metabolic disorder? Med Princ Pract. 2014;23(5):421–5.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Madani H, Rahimi Z, Manavi-Shad M, Mozafari H, Akramipour R, Vaisi-Raygani A, et al. Plasma lipids and lipoproteins in children and young adults with major β-thalassemia from western Iran: influence of genotype. Mol Biol Rep. 2011;38(4):2573–8.

    Article  CAS  PubMed  Google Scholar 

  33. Maioli M, Vigna GB, Tonolo G, Brizzi P, Ciccarese M, Donegà P, Maioli M, Fellin R. Plasma lipoprotein composition, apolipoprotein(a) concentration and isoforms in beta-thalassemia. Atherosclerosis. 1997;131(1):127–33.

    Article  CAS  PubMed  Google Scholar 

  34. Saki F, Bahadori R, Kashkooli NM, Jazayeri A, Ghahremani N, Omrani GHR. Prevalence of metabolic syndrome in beta thalassemia major adolescents in southern Iran: a cross-sectional study. Int J Diabetes Dev Ctries. 2019;39(3):444–50.

    Article  CAS  Google Scholar 

  35. Setoodeh S, Khorsand M, Takhshid MA. The effects of iron overload, insulin resistance and oxidative stress on metabolic disorders in patients with β- thalassemia major. J Diabetes Metab Disord. 2020;19(2):767–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Tantawy AAG, Adly AAM, El Maaty MGA, Amin SAG. Subclinical atherosclerosis in young β-thalassemia major patients. Hemoglobin. 2009;33(6):463–74.

    Article  CAS  PubMed  Google Scholar 

  37. Dantas MT, Lopes A, Ladeia AMT. Association between lipid Profile and Clinical manifestations in Sickle Cell Anemia: a systematic review. Int J Cardiovasc Sci. 2022;35(6):770–9.

    Google Scholar 

  38. Zorca S, Freeman L, Hildesheim M, Allen D, Remaley AT, Taylor JG, et al. Lipid levels in sickle-cell disease associated with haemolytic severity, vascular dysfunction and pulmonary hypertension. Br J Haematol. 2010;149(3):436–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zorca SM, Freeman LA, Littel PL, Kato GJ. Hypocholesterolemia in a large sickle cell cohort: correlations of serum lipids to markers of intravascular hemolysis and vascular dysfunction. Blood. 2008;112(11):123–123.

    Article  Google Scholar 

  40. Yalcinkaya A, Unal S, Oztas Y. Altered HDL particle in sickle cell disease: decreased cholesterol content is associated with hemolysis, whereas decreased apolipoprotein A1 is linked to inflammation. Lipids Health Dis. 2019;18(1):225.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Khalifa AS, Salem M, Mounir E, El-Tawil MM, El-Sawy M, Abd Al-Aziz MM. Abnormal glucose tolerance in Egyptian beta-thalassemic patients: possible association with genotyping. Pediatr Diabetes. 2004;5(3):126–32.

    Article  CAS  PubMed  Google Scholar 

  42. Kang HT, Linton JA, Shim JY. Serum ferritin level is associated with the prevalence of metabolic syndrome in Korean adults: the 2007–2008 Korean National Health and Nutrition Examination Survey. Clin Chim Acta. 2012;413(5–6):636–41.

    Article  CAS  PubMed  Google Scholar 

  43. Jehn M, Clark JM, Guallar E. Serum ferritin and risk of the metabolic syndrome in U.S. adults. Diabetes Care. 2004;27(10).

  44. Calandra S, Deiana L, Volti SL. b-Thalassemia is a modifying factor of the clinical expression of familial hypercholesterolemia. Semin Vasc Med. 2004;4(3).

  45. Deiana L, Garuti R, Pes GM, Carru C, Errigo A, Rolleri M, Pisciotta L, Masturzo P, Cantafora A, Calandra S, Bertolini S. Influence of beta(0)-thalassemia on the phenotypic expression of heterozygous familial hypercholesterolemia: a study of patients with familial hypercholesterolemia from Sardinia. Arterioscler Thromb Vasc Biol. 2000;20(1):236–43.

    Article  CAS  PubMed  Google Scholar 

  46. Lai ME, Vacquer S, Carta MP, Spiga A, Cocco P, Angius F, et al. Thalassemia intermedia is associated with a proatherogenic biochemical phenotype. Blood Cells Mol Dis. 2011;46(4):294–9.

    Article  CAS  PubMed  Google Scholar 

  47. Hoda AI, Soha SZ, Manal ME, Mohamed RE, Samia AEE. New insight on premature atherosclerosis in Egyptian children with -thalassemia major. Afr J Biochem Res. 2018;12(9):86–93.

    Article  Google Scholar 

  48. Ray S, Saikia D, Vashisht Y, Sharma S, Meena RK, Kumar M. Dyslipidemia and atherogenic indexes in children with transfusion-dependent thalassemia. Hematol Transfus Cell Ther. 2024 Oct-Dec;46(4):345–51.

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Authors and Affiliations

  1. Faculty of Medicine, University of Khartoum, P.O. Box 302, Khartoum, Sudan

    Sagad O. O. Mohamed, Ali E.A. Mohamed, Ahmed S.E.E. Abdelrahman, Ahmed G.A. Abdelgadir, Mona G.A. Ahmedkaroum, Gehad A. Abdalla & Hanaa A.M. Fadil

  2. Harlem Hospital Center, New York, NY, US

    Mohamed S.K. Salih

  3. United Lincolnshire Hospitals Trust, Lincolnshire, UK

    Khalid S.K. Salih

  4. Faculty of Medicine, Nile University, Khartoum, Sudan

    Mahmoud A.M. Abdelrahman

  5. Faculty of Medicine, University of Bahri, Khartoum, Sudan

    Nehal S.A. Salih

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  1. Sagad O. O. Mohamed
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Contributions

SM conceptualized the research idea. SM, MS, and KS undertook database searches and articles screening. AEM, ASA, MAA, MGA, AGA and GAA undertook quality assessment and data extraction. SM undertook data analysis. SM, HF and NS interpreted the results and drafted the manuscript. All authors revised, edited and approved the final manuscript.

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Correspondence to Sagad O. O. Mohamed.

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Electronic supplementary material

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12944_2024_2377_MOESM1_ESM.docx

Supplementary Material 1: Figure S1. Funnel plots illustrate the assessment of publication bias for the results of beta-thalassemia major.

12944_2024_2377_MOESM2_ESM.docx

Supplementary Material 2: Figure S2. Funnel plots illustrate the assessment of publication bias for the results of beta-thalassemia intermedia.

12944_2024_2377_MOESM3_ESM.docx

Supplementary Material 3: Figure S3. Funnel plots illustrate the assessment of publication bias for the results of beta-thalassemia minor.

Supplementary Material 4: Table S1: Detailed assessment of certainty of evidence for each outcome.

Supplementary Material 5

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Mohamed, S.O.O., Mohamed, A.E., Salih, M.S. et al. Serum lipid profile abnormalities among beta-thalassemia patients: a systematic review and meta-analysis. Lipids Health Dis 23, 388 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02377-6

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02377-6

Keywords

Lipids in Health and Disease

ISSN: 1476-511X