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Liver Iron Overload Drives COVID-19 Mortality: a Two-Sample Mendelian Randomization Study

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Abstract

Iron overload has been associated with an increased risk of COVID-19 severity and mortality in observational studies, but it remains unclear whether these associations represent causal effects. We performed a two-sample Mendelian randomization (MR) to determine associations between genetic liability to iron overload and the risk of COVID-19 severity and mortality. From genome-wide association studies of European ancestry, single-nucleotide polymorphisms associated with liver iron (n = 32,858) and ferritin (n = 23,986) were selected as exposure instruments, and summary statistics of the hospitalization (n = 16,551) and mortality (n = 15,815) of COVID-19 were utilized as the outcome. We used the inverse-variance weighted (IVW) method as the primary analysis to estimate causal effects, and other alternative approaches as well as comprehensive sensitivity analysis were conducted for estimating the robustness of identified associations. Genetically predicted high liver iron levels were associated with an increased risk of COVID-19 mortality based on the results of IVW analysis (OR = 1.38, 95% CI: 1.05–1.82, P = 0.02). Likewise, sensitivity analyses showed consistent and robust results in general (all P > 0.05). A higher risk of COVID-19 hospitalization trend was also observed in patients with high liver iron levels without statistical significance. This study suggests that COVID-19 mortality might be partially driven by the iron accumulation in the liver, supporting the classification of iron overload as one of the independent death risk factors. Therefore, avoiding iron overload and maintaining normal iron levels may be a powerful measure to reduce COVID-19 mortality.

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Data Availability

Data described in the manuscript can be freely downloaded from the IEU OpenGWAS project (https://gwas.mrcieu.ac.uk/) and https://grasp.nhlbi.nih.gov/Covid19GWASResults.aspx.

References

  1. Koelle K, Martin MA, Antia R, Lopman B, Dean NE (2022) The changing epidemiology of SARS-CoV-2. Science 375(6585):1116–1121. https://doi.org/10.1126/science.abm4915

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395(10229):1054–1062. https://doi.org/10.1016/s0140-6736(20)30566-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wu Z, McGoogan JM (2020) Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese center for disease control and prevention. JAMA 323(13):1239–1242. https://doi.org/10.1001/jama.2020.2648

    Article  CAS  PubMed  Google Scholar 

  4. Ganz T, Nemeth E (2015) Iron homeostasis in host defence and inflammation. Nat Rev Immunol 15(8):500–510. https://doi.org/10.1038/nri3863

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Lucijanic M, Demaria M, Gnjidic J, Rob Z, Filipovic D, Penovic T, Jordan A, Barisic-Jaman M, Pastrović F, Lucijanic D, Cikara T, Lucijanic T, Miletic M, Ljubicic D, Keres T (2022) Higher ferritin levels in COVID-19 patients are associated with hyperinflammation, worse prognosis, and more bacterial infections without pronounced features of hemophagocytosis. Ann Hematol 101(5):1119–1121. https://doi.org/10.1007/s00277-022-04813-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223):497–506. https://doi.org/10.1016/s0140-6736(20)30183-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Huang I, Pranata R, Lim MA, Oehadian A, Alisjahbana B (2020) C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease-2019: a meta-analysis. Ther Adv Respir Dis 14:1753466620937175. https://doi.org/10.1177/1753466620937175

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Mohus RM, Flatby H, Liyanarachi KV, DeWan AT, Solligård E, Damås JK, Åsvold BO, Gustad LT, Rogne T (2022) Iron status and the risk of sepsis and severe COVID-19: a two-sample Mendelian randomization study. Sci Rep 12(1):16157. https://doi.org/10.1038/s41598-022-20679-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zeng F, Huang Y, Guo Y, Yin M, Chen X, Xiao L, Deng G (2020) Association of inflammatory markers with the severity of COVID-19: a meta-analysis. Int J Infect Dis 96:467–474. https://doi.org/10.1016/j.ijid.2020.05.055

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hartwig FP, Davies NM, Hemani G, Davey Smith G (2016) Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol 45(6):1717–1726. https://doi.org/10.1093/ije/dyx028

    Article  PubMed  Google Scholar 

  11. Davies NM, Holmes MV, Davey Smith G (2018) Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ 362:k601. https://doi.org/10.1136/bmj.k601

    Article  PubMed  PubMed Central  Google Scholar 

  12. Bonkovsky HL (1991) Iron and the liver. Am J Med Sci 301(1):32–43. https://doi.org/10.1097/00000441-199101000-00006

    Article  CAS  PubMed  Google Scholar 

  13. Liu Y, Basty N, Whitcher B, Bell JD, Sorokin EP, van Bruggen N, Thomas EL, Cule M (2021) Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning. Elife 10:e65554. https://doi.org/10.7554/eLife.65554

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Benyamin B, Esko T, Ried JS, Radhakrishnan A, Vermeulen SH, Traglia M, Gögele M, Anderson D, Broer L, Podmore C, Luan J, Kutalik Z, Sanna S, van der Meer P, Tanaka T, Wang F, Westra HJ, Franke L, Mihailov E, Milani L, Hälldin J, Winkelmann J, Meitinger T, Thiery J, Peters A, Waldenberger M, Rendon A, Jolley J, Sambrook J, Kiemeney LA, Sweep FC, Sala CF, Schwienbacher C, Pichler I, Hui J, Demirkan A, Isaacs A, Amin N, Steri M, Waeber G, Verweij N, Powell JE, Nyholt DR, Heath AC, Madden PA, Visscher PM, Wright MJ, Montgomery GW, Martin NG, Hernandez D, Bandinelli S, van der Harst P, Uda M, Vollenweider P, Scott RA, Langenberg C, Wareham NJ, van Duijn C, Beilby J, Pramstaller PP, Hicks AA, Ouwehand WH, Oexle K, Gieger C, Metspalu A, Camaschella C, Toniolo D, Swinkels DW, Whitfield JB (2014) Novel loci affecting iron homeostasis and their effects in individuals at risk for hemochromatosis. Nat Commun 5:4926. https://doi.org/10.1038/ncomms5926

    Article  CAS  PubMed  Google Scholar 

  15. Thibord F, Chan MV, Chen MH, Johnson AD (2022) A year of COVID-19 GWAS results from the GRASP portal reveals potential genetic risk factors. HGG Adv 3(2):100095. https://doi.org/10.1016/j.xhgg.2022.100095

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Burgess S, Thompson SG (2011) Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol 40(3):755–764. https://doi.org/10.1093/ije/dyr036

    Article  PubMed  Google Scholar 

  17. Burgess S, Butterworth A, Thompson SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37(7):658–665. https://doi.org/10.1002/gepi.21758

    Article  PubMed  PubMed Central  Google Scholar 

  18. Bowden J, Davey Smith G, Haycock PC, Burgess S (2016) Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 40(4):304–314. https://doi.org/10.1002/gepi.21965

    Article  PubMed  PubMed Central  Google Scholar 

  19. Bowden J, Davey Smith G, Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44(2):512–525. https://doi.org/10.1093/ije/dyv080

    Article  PubMed  PubMed Central  Google Scholar 

  20. Burgess S, Thompson SG (2017) Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32(5):377–389. https://doi.org/10.1007/s10654-017-0255-x

    Article  PubMed  PubMed Central  Google Scholar 

  21. Verbanck M, Chen CY, Neale B, Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50(5):693–698. https://doi.org/10.1038/s41588-018-0099-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bowden J, Hemani G, Davey Smith G (2018) Invited commentary: detecting individual and global horizontal pleiotropy in Mendelian randomization-a job for the humble heterogeneity statistic? Am J Epidemiol 187(12):2681–2685. https://doi.org/10.1093/aje/kwy185

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, Timpson NJ, Evans DM, Relton C, Martin RM, Davey Smith G, Gaunt TR, Haycock PC (2018) The MR-base platform supports systematic causal inference across the human phenome. Elife 30(7):e34408. https://doi.org/10.7554/eLife.34408

    Article  Google Scholar 

  24. Wood JC (2007) Diagnosis and management of transfusion iron overload: the role of imaging. Am J Hematol 82(12 Suppl):1132–1135. https://doi.org/10.1002/ajh.21099

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Garcia-Casal MN, Pasricha SR, Martinez RX, Lopez-Perez L, Peña-Rosas JP (2018) Are current serum and plasma ferritin cut-offs for iron deficiency and overload accurate and reflecting iron status? A systematic review. Arch Med Res 49(6):405–417. https://doi.org/10.1016/j.arcmed.2018.12.005

    Article  CAS  PubMed  Google Scholar 

  26. Li X, Xu S, Yu M, Wang K, Tao Y, Zhou Y, Shi J, Zhou M, Wu B, Yang Z, Zhang C, Yue J, Zhang Z, Renz H, Liu X, Xie J, Xie M, Zhao J (2020) Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. J Allergy Clin Immunol 146(1):110–118. https://doi.org/10.1016/j.jaci.2020.04.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sonnweber T, Boehm A, Sahanic S, Pizzini A, Aichner M, Sonnweber B, Kurz K, Koppelstätter S, Haschka D, Petzer V, Hilbe R, Theurl M, Lehner D, Nairz M, Puchner B, Luger A, Schwabl C, Bellmann-Weiler R, Wöll E, Widmann G, Tancevski I, Judith Löffler R, Weiss G (2020) Persisting alterations of iron homeostasis in COVID-19 are associated with non-resolving lung pathologies and poor patients’ performance: a prospective observational cohort study. Respir Res 21(1):276. https://doi.org/10.1186/s12931-020-01546-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Merad M, Martin JC (2020) Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages. Nat Rev Immunol 20(6):355–362. https://doi.org/10.1038/s41577-020-0331-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Tsitsikas DA, Nzouakou R, Ameen V, Sirigireddy B, Amos RJ (2014) Comparison of serial serum ferritin measurements and liver iron concentration assessed by MRI in adult transfused patients with sickle cell disease. Eur J Haematol 92(2):164–167. https://doi.org/10.1111/ejh.12230

    Article  CAS  PubMed  Google Scholar 

  30. Trottier BJ, Burns LJ, DeFor TE, Cooley S, Majhail NS (2013) Association of iron overload with allogeneic hematopoietic cell transplantation outcomes: a prospective cohort study using R2-MRI-measured liver iron content. Blood 122(9):1678–1684. https://doi.org/10.1182/blood-2013-04-499772

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Angelucci E, Brittenham GM, McLaren CE, Ripalti M, Baronciani D, Giardini C, Galimberti M, Polchi P, Lucarelli G (2000) Hepatic iron concentration and total body iron stores in thalassemia major. N Engl J Med 343(5):327–331. https://doi.org/10.1056/nejm200008033430503

    Article  CAS  PubMed  Google Scholar 

  32. Reeder SB, Yokoo T, França M, Hernando D, Alberich-Bayarri Á, Alústiza JM, Gandon Y, Henninger B, Hillenbrand C, Jhaveri K, Karçaaltıncaba M, Kühn JP, Mojtahed A, Serai SD, Ward R, Wood JC, Yamamura J, Martí-Bonmatí L (2023) Quantification of liver iron overload with mri: review and guidelines from the ESGAR and SAR. Radiology 307(1):e221856. https://doi.org/10.1148/radiol.221856

    Article  PubMed  Google Scholar 

  33. Obrzut M, Atamaniuk V, Glaser KJ, Chen J, Ehman RL, Obrzut B, Cholewa M, Gutkowski K (2020) Value of liver iron concentration in healthy volunteers assessed by MRI. Sci Rep 10(1):17887. https://doi.org/10.1038/s41598-020-74968-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Radushkevitz-Frishman T, Charni-Natan M, Goldstein I (2023) Dynamic chromatin accessibility during nutritional iron overload reveals a BMP6-independent induction of cell cycle genes. J Nutr Biochem 119:109407. https://doi.org/10.1016/j.jnutbio.2023.109407

    Article  CAS  PubMed  Google Scholar 

  35. Kohgo Y, Ikuta K, Ohtake T, Torimoto Y, Kato J (2008) Body iron metabolism and pathophysiology of iron overload. Int J Hematol 88(1):7–15. https://doi.org/10.1007/s12185-008-0120-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Rostoker G, Griuncelli M, Loridon C, Magna T, Machado G, Drahi G, Dahan H, Janklewicz P, Cohen Y (2015) Reassessment of iron biomarkers for prediction of dialysis iron overload: an MRI study. PLoS One 10(7):e0132006. https://doi.org/10.1371/journal.pone.0132006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Pietrangelo A (2016) Iron and the liver. Liver Int 36(Suppl 1):116–123. https://doi.org/10.1111/liv.13020

    Article  CAS  PubMed  Google Scholar 

  38. Moreira AC, Neves JV, Silva T, Oliveira P, Gomes MS, Rodrigues PN (2017) Hepcidin-(In)dependent mechanisms of iron metabolism regulation during infection by listeria and salmonella. Infect Immun 85(9):e00353–17. https://doi.org/10.1128/iai.00353-17

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Moreira AC, Silva T, Mesquita G, Gomes AC, Bento CM, Neves JV, Rodrigues DF, Rodrigues PN, Almeida AA, Santambrogio P, Gomes MS (2021) H-ferritin produced by myeloid cells is released to the circulation and plays a major role in liver iron distribution during infection. Int J Mol Sci 23(1):269. https://doi.org/10.3390/ijms23010269

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Del Nonno F, Nardacci R, Colombo D, Visco-Comandini U, Cicalini S, Antinori A, Marchioni L, D’Offizi G, Piacentini M, Falasca L (2021) Hepatic failure in COVID-19: is iron overload the dangerous trigger? Cells 10(5):1103. https://doi.org/10.3390/cells10051103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Hasan SS, Capstick T, Ahmed R, Kow CS, Mazhar F, Merchant HA, Zaidi STR (2020) Mortality in COVID-19 patients with acute respiratory distress syndrome and corticosteroids use: a systematic review and meta-analysis. Expert Rev Respir Med 14(11):1149–1163. https://doi.org/10.1080/17476348.2020.1804365

    Article  CAS  PubMed  Google Scholar 

  42. Sheervalilou R, Shirvaliloo M, Dadashzadeh N, Shirvalilou S, Shahraki O, Pilehvar-Soltanahmadi Y, Ghaznavi H, Khoei S, Nazarlou Z (2020) COVID-19 under spotlight: a close look at the origin, transmission, diagnosis, and treatment of the 2019-nCoV disease. J Cell Physiol 235(12):8873–8924. https://doi.org/10.1002/jcp.29735

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Diao B, Wang C, Tan Y, Chen X, Liu Y, Ning L, Chen L, Li M, Liu Y, Wang G, Yuan Z, Feng Z, Zhang Y, Wu Y, Chen Y (2020) Reduction and functional exhaustion of T cells in patients with coronavirus disease 2019 (COVID-19). Front Immunol 11:827. https://doi.org/10.3389/fimmu.2020.00827

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Cassat JE, Skaar EP (2013) Iron in infection and immunity. Cell Host Microbe 13(5):509–519. https://doi.org/10.1016/j.chom.2013.04.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Moreira AC, Mesquita G, Gomes MS (2020) Ferritin: an inflammatory player keeping iron at the core of pathogen-host interactions. Microorganisms 8(4):589. https://doi.org/10.3390/microorganisms8040589

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Nakamura K, Kawakami T, Yamamoto N, Tomizawa M, Fujiwara T, Ishii T, Harigae H, Ogasawara K (2016) Activation of the NLRP3 inflammasome by cellular labile iron. Exp Hematol 44(2):116–124. https://doi.org/10.1016/j.exphem.2015.11.002

    Article  CAS  PubMed  Google Scholar 

  47. Ruddell RG, Hoang-Le D, Barwood JM, Rutherford PS, Piva TJ, Watters DJ, Santambrogio P, Arosio P, Ramm GA (2009) Ferritin functions as a proinflammatory cytokine via iron-independent protein kinase C zeta/nuclear factor kappaB-regulated signaling in rat hepatic stellate cells. Hepatology 49(3):887–900. https://doi.org/10.1002/hep.22716

    Article  CAS  PubMed  Google Scholar 

  48. Proneth B, Conrad M (2019) Ferroptosis and necroinflammation, a yet poorly explored link. Cell Death Differ 26(1):14–24. https://doi.org/10.1038/s41418-018-0173-9

    Article  CAS  PubMed  Google Scholar 

  49. Xu Z, Shi L, Wang Y, Zhang J, Huang L, Zhang C, Liu S, Zhao P, Liu H, Zhu L, Tai Y, Bai C, Gao T, Song J, Xia P, Dong J, Zhao J, Wang FS (2020) Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med 8(4):420–422. https://doi.org/10.1016/s2213-2600(20)30076-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Gardenghi G (2020) Pathophysiology of worsening lung function in COVID-19. Rev Bras Fisiol Exerc 19(2):40–46. https://doi.org/10.33233/rbfe.v19i2.4058

    Article  Google Scholar 

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Acknowledgements

The authors sincerely thank the Dr. Liu, Dr. Benyamin, Dr. Thibord et al., UK Biobank, Genetics of Iron Status, and all concerned investigators for sharing GWAS summary statistics.

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Authors and Affiliations

  1. Zhonglou District Center for Disease Control and Prevention, Changzhou, Jiangsu, China

    Huimin Tian

  2. Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China

    Xiangjie Kong, Fulei Han, Fangjie Xing, Shuai Zhu, Weijing Wang & Yili Wu

  3. Department of Pulmonary and Critical Care Medicine, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China

    Tao Xu

  4. Department of Physiology, Shandong Provincial Key Laboratory of Pathogenesis and Prevention of Neurological Disorder, School of Basic Medicine, Qingdao University, Qingdao, Shandong, China

    Ning Song

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  1. Huimin Tian
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Contributions

Ning Song and Yili Wu contributed to the study conception and design. Material preparation, data collection and analysis were performed by Huimin Tian, Xiangjie Kong, Fulei Han, Fangjie Xing, Shuai Zhu, Tao Xu and Weijing Wang. The first draft of the manuscript was written by Huimin Tian and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Tian, H., Kong, X., Han, F. et al. Liver Iron Overload Drives COVID-19 Mortality: a Two-Sample Mendelian Randomization Study. Biol Trace Elem Res 202, 2509–2517 (2024). https://doi.org/10.1007/s12011-023-03878-8

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