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Nutrition in acute and chronic diseases

Causal association of genetically determined circulating vitamin D metabolites and calcium with multiple sclerosis in participants of European descent

Abstract

Background

Vitamin D is an important regulator of calcium. Mendelian randomization (MR) studies exclusively focused on the circulating total 25-hydroxyvitamin D (25(OH)D) as a biomarker of vitamin D status, and have found the causal association between 25(OH)D and the risk of multiple sclerosis (MS). However, it currently remains unclear about the causal association of the 25(OH)D subtypes including 25(OH)D3 and C3-epi-25(OH)D3, as well as calcium with the risk of MS.

Methods

We performed a two-sample MR study to evaluate the causal association of circulating total 25(OH)D, 25(OH)D3, C3-epi-25(OH)D3, and calcium with the risk of MS using large-scale genome-wide association studies (GWAS) datasets from total 25(OH)D (n = 417,580), 25(OH)D3 (n = 40,562), C3-epi-25(OH)D3 (n = 40,562), calcium (n = 305,349), and MS (14,802 MS and 26,703 controls). We selected five MR methods including inverse-variance weighted (IVW), simple median, weighted median, MR-Egger, MR-PRESSO (Mendelian Randomization Pleiotropy Residual Sum and Outlier), and contamination mixture method.

Results

IVW showed that the genetically increased circulating 25(OH)D level (OR = 0.81, 95% CI: 0.70–0.94, P = 4.00E-03), circulating 25(OH)D3 level (OR = 0.85, 95% CI: 0.76–0.95, P = 5.00E-03), and circulating C3-epi-25(OH)D3 level (OR = 0.85, 95% CI: 0.74–0.98, P = 2.30E-02) were causally associated with reduced risk of MS. However, IVW showed no causal association between circulating calcium level and the risk of MS with OR = 2.85, 95% CI: 0.42–19.53, P = 2.85E-01.

Conclusions

Our current findings together with evidence from other MR studies support the use of vitamin D but not calcium supplementation for the prevention of MS.

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Fig. 1: The flow chart about the MR study design.
Fig. 2: Individual estimates about the causal effect of circulating 25(OH)D on MS using IVW method.
Fig. 3: Individual estimates about the causal effect of circulating 25(OH)D3 on MS using IVW method.

Data availability

All data generated or analyzed during this study are included in this published article and its Additional files. The authors confirm that all data underlying the findings are either fully available without restriction through consortia websites, or may be made available from consortia upon request. International Multiple Sclerosis Genetics Consortium (IMSGC): https://imsgc.net/.

References

  1. Liu G, Zhang F, Jiang Y, Hu Y, Gong Z, Liu S, et al. Integrating genome-wide association studies and gene expression data highlights dysregulated multiple sclerosis risk pathways. Mult Scler. 2017;23:205–12.

    Article  CAS  PubMed  Google Scholar 

  2. Munger KL, Zhang SM, O’Reilly E, Hernan MA, Olek MJ, Willett WC, et al. Vitamin D intake and incidence of multiple sclerosis. Neurology 2004;62:60–65.

    Article  CAS  PubMed  Google Scholar 

  3. Munger KL, Hongell K, Aivo J, Soilu-Hanninen M, Surcel HM, Ascherio A. 25-Hydroxyvitamin D deficiency and risk of MS among women in the Finnish Maternity Cohort. Neurology 2017;89:1578–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Salzer J, Hallmans G, Nystrom M, Stenlund H, Wadell G, Sundstrom P. Vitamin D as a protective factor in multiple sclerosis. Neurology 2012;79:2140–5.

    Article  CAS  PubMed  Google Scholar 

  5. Munger KL, Levin LI, Hollis BW, Howard NS, Ascherio A. Serum 25-hydroxyvitamin D levels and risk of multiple sclerosis. JAMA 2006;296:2832–8.

    Article  CAS  PubMed  Google Scholar 

  6. Harroud A, Richards JB. Mendelian randomization in multiple sclerosis: A causal role for vitamin D and obesity? Mult Scler. 2018;24:80–85.

    Article  CAS  PubMed  Google Scholar 

  7. Mokry LE, Ross S, Ahmad OS, Forgetta V, Smith GD, Goltzman D, et al. Vitamin D and Risk of Multiple Sclerosis: A Mendelian Randomization Study. PLoS Med. 2015;12:e1001866.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Rhead B, Baarnhielm M, Gianfrancesco M, Mok A, Shao X, Quach H, et al. Mendelian randomization shows a causal effect of low vitamin D on multiple sclerosis risk. Neurol Genet. 2016;2:e97.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Gianfrancesco MA, Stridh P, Rhead B, Shao X, Xu E, Graves JS, et al. Evidence for a causal relationship between low vitamin D, high BMI, and pediatric-onset MS. Neurology 2017;88:1623–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jacobs BM, Noyce AJ, Giovannoni G, Dobson R. BMI and low vitamin D are causal factors for multiple sclerosis: A Mendelian Randomization study. Neurol Neuroimmunol Neuroinflamm. 2020;7:e662.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Harroud A, Manousaki D, Butler-Laporte G, Mitchell RE, Davey Smith G, Richards JB, et al. The relative contributions of obesity, vitamin D, leptin, and adiponectin to multiple sclerosis risk: A Mendelian randomization mediation analysis. Mult Scler. 2021;27:1994–2000.

    Article  CAS  PubMed  Google Scholar 

  12. Bouillon R, Manousaki D, Rosen C, Trajanoska K, Rivadeneira F, Richards JB. The health effects of vitamin D supplementation: Evidence from human studies. Nat Rev Endocrinol. 2022;18:96–110.

    Article  CAS  PubMed  Google Scholar 

  13. Wang R. Mendelian randomization study updates the effect of 25-hydroxyvitamin D levels on the risk of multiple sclerosis. J Transl Med. 2022;20:3.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Vandebergh M, Dubois B, Goris A. Effects of Vitamin D and body mass index on disease risk and relapse hazard in multiple sclerosis: A mendelian randomization study. Neurol Neuroimmunol Neuroinflamm. 2022;9:e1165.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Zheng JS, Imamura F, Sharp SJ, van der Schouw YT, Sluijs I, Gundersen TE, et al. Association of plasma vitamin D metabolites with incident type 2 diabetes: EPIC-InterAct case-cohort study. J Clin Endocrinol Metab. 2019;104:1293–303.

    Article  PubMed  Google Scholar 

  16. Steingrimsdottir L, Gunnarsson O, Indridason OS, Franzson L, Sigurdsson G. Relationship between serum parathyroid hormone levels, vitamin D sufficiency, and calcium intake. JAMA. 2005;294:2336–41.

    Article  CAS  PubMed  Google Scholar 

  17. Arnold A, Dennison E, Kovacs CS, Mannstadt M, Rizzoli R, Brandi ML, et al. Hormonal regulation of biomineralization. Nat Rev Endocrinol. 2021;17:261–75.

    Article  CAS  PubMed  Google Scholar 

  18. Kern J, Kern S, Blennow K, Zetterberg H, Waern M, Guo X, et al. Calcium supplementation and risk of dementia in women with cerebrovascular disease. Neurology 2016;87:1674–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhang FF, Barr SI, McNulty H, Li D, Blumberg JB. Health effects of vitamin and mineral supplements. BMJ. 2020;369:m2511.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Harvey NC, D’Angelo S, Paccou J, Curtis EM, Edwards M, Raisi-Estabragh Z, et al. Calcium and Vitamin D supplementation are not associated with risk of incident ischemic cardiac events or death: Findings from the UK biobank cohort. J Bone Min Res. 2018;33:803–11.

    Article  CAS  Google Scholar 

  21. Jian Z, Huang Y, He Y, Jin X, Li H, Li S, et al. Genetically predicted lifelong circulating 25(OH)D levels are associated with serum calcium levels and kidney stone risk. J Clin Endocrinol Metab. 2022;107:e1159–e1166.

    Article  PubMed  Google Scholar 

  22. Revez JA, Lin T, Qiao Z, Xue A, Holtz Y, Zhu Z, et al. Genome-wide association study identifies 143 loci associated with 25 hydroxyvitamin D concentration. Nat Commun. 2020;11:1647.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zheng JS, Luan J, Sofianopoulou E, Sharp SJ, Day FR, Imamura F, et al. The association between circulating 25-hydroxyvitamin D metabolites and type 2 diabetes in European populations: A meta-analysis and Mendelian randomisation analysis. PLoS Med. 2020;17:e1003394.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Young WJ, Warren HR, Mook-Kanamori DO, Ramirez J, van Duijvenboden S, Orini M, et al. Genetically determined serum calcium levels and markers of ventricular repolarization: a mendelian randomization study in the UK Biobank. Circ Genom Precis Med. 2021;14:e003231.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 2019; 365(6460):eaav7188.

  26. Liu G, Zhao Y, Jin S, Hu Y, Wang T, Tian R, et al. Circulating vitamin E levels and Alzheimer’s disease: A Mendelian randomization study. Neurobiol Aging. 2018;72:189 e181–189 e189.

    Article  Google Scholar 

  27. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377–89.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Bowden J, Davey, Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304–14.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Burgess S, Foley CN, Allara E, Staley JR, Howson JMM. A robust and efficient method for Mendelian randomization with hundreds of genetic variants. Nat Commun. 2020;11:376.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Bowden J, Holmes MV. Meta-analysis and Mendelian randomization: A review. Res Synth Methods. 2019;10:486–96.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Yavorska OO, Burgess S. MendelianRandomization: An R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46:1734–9.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Shim H, Chasman DI, Smith JD, Mora S, Ridker PM, Nickerson DA, et al. A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians. PLoS One. 2015;10:e0120758.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40:740–52.

    Article  PubMed  Google Scholar 

  35. Palmer TM, Lawlor DA, Harbord RM, Sheehan NA, Tobias JH, Timpson NJ, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res. 2012;21:223–42.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Burgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40:755–64.

    Article  PubMed  Google Scholar 

  37. Brion MJ, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013;42:1497–501.

    Article  PubMed  Google Scholar 

  38. Ward LD, Kellis M. HaploReg: A resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40:D930–934.

    Article  CAS  PubMed  Google Scholar 

  39. Maretzke F, Bechthold A, Egert S, Ernst JB, Melo van Lent D, Pilz S, et al. Role of Vitamin D in preventing and treating selected extraskeletal diseases-an umbrella review. Nutrients. 2020;12:969.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Boltjes R, Knippenberg S, Gerlach O, Hupperts R, Damoiseaux J. Vitamin D supplementation in multiple sclerosis: An expert opinion based on the review of current evidence. Expert Rev Neurother. 2021;21:715–25.

    Article  CAS  PubMed  Google Scholar 

  41. Feige J, Moser T, Bieler L, Schwenker K, Hauer L, Sellner J. Vitamin D supplementation in multiple sclerosis: A critical analysis of potentials and threats. Nutrients. 2020;12:783.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Fatima M, Lamis A, Siddiqui SW, Ashok T, Patni N, Fadiora OE. Therapeutic role of Vitamin D in multiple sclerosis: An essentially contested concept. Cureus 2022;14:e26186.

    PubMed  PubMed Central  Google Scholar 

  43. Dorr J, Backer-Koduah P, Wernecke KD, Becker E, Hoffmann F, Faiss J, et al. High-dose vitamin D supplementation in multiple sclerosis - results from the randomized EVIDIMS (efficacy of vitamin D supplementation in multiple sclerosis) trial. Mult Scler J Exp Transl Clin. 2020;6:2055217320903474.

    PubMed  PubMed Central  Google Scholar 

  44. Backer-Koduah P, Bellmann-Strobl J, Scheel M, Wuerfel J, Wernecke KD, Dorr J, et al. Vitamin D and disease severity in multiple sclerosis-baseline data from the randomized controlled trial (EVIDIMS). Front Neurol. 2020;11:129.

    Article  PubMed  PubMed Central  Google Scholar 

  45. McLaughlin L, Clarke L, Khalilidehkordi E, Butzkueven H, Taylor B, Broadley SA. Vitamin D for the treatment of multiple sclerosis: A meta-analysis. J Neurol. 2018;265:2893–905.

    Article  CAS  PubMed  Google Scholar 

  46. Galoppin M, Kari S, Soldati S, Pal A, Rival M, Engelhardt B, et al. Full spectrum of vitamin D immunomodulation in multiple sclerosis: Mechanisms and therapeutic implications. Brain Commun. 2022;4:fcac171.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Mao D, Yuen LY, Ho CS, Wang CC, Tam CH, Chan MH, et al. Maternal and Neonatal 3-epi-25-hydroxyvitamin D concentration and factors influencing their concentrations. J Endocr Soc. 2022;6:bvab170.

    Article  PubMed  Google Scholar 

  48. Kubiak JM, Grimnes G, Cashman KD, Kamycheva E, Dowling K, Skrabakova Z, et al. C3-epimerization of 25-hydroxyvitamin D increases with increasing serum 25-hydroxyvitamin D levels and shows a high degree of tracking over time. Clin Biochem. 2018;54:61–7.

    Article  CAS  PubMed  Google Scholar 

  49. Yuan S, Jiang X, Michaelsson K, Larsson SC. Genetic Prediction of Serum 25-Hydroxyvitamin D, Calcium, and Parathyroid hormone levels in relation to development of type 2 diabetes: A mendelian randomization study. Diabetes Care. 2019;42:2197–203.

    Article  CAS  PubMed  Google Scholar 

  50. Schwid SR, Goodman AD, Puzas JE, McDermott MP, Mattson DH. Sporadic corticosteroid pulses and osteoporosis in multiple sclerosis. Arch Neurol. 1996;53:753–7.

    Article  CAS  PubMed  Google Scholar 

  51. Bazelier MT, van Staa T, Uitdehaag BM, Cooper C, Leufkens HG, Vestergaard P, et al. The risk of fracture in patients with multiple sclerosis: the UK general practice research database. J Bone Min Res. 2011;26:2271–9.

    Article  Google Scholar 

  52. Bisson EJ, Finlayson ML, Ekuma O, Leslie WD, Marrie RA. Multiple sclerosis is associated with low bone mineral density and osteoporosis. Neurol Clin Pr. 2019;9:391–9.

    Article  Google Scholar 

  53. Cerani A, Zhou S, Forgetta V, Morris JA, Trajanoska K, Rivadeneira F, et al. Genetic predisposition to increased serum calcium, bone mineral density, and fracture risk in individuals with normal calcium levels: mendelian randomisation study. BMJ. 2019;366:l4410.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Li GH, Robinson-Cohen C, Sahni S, Au PC, Tan KC, Kung AW, et al. Association of genetic variants related to serum calcium levels with reduced bone mineral density. J Clin Endocrinol Metab. 2020;105:e328–e36.

    Article  PubMed  Google Scholar 

  55. Qu Z, Yang F, Yan Y, Hong J, Wang W, Li S, et al. Relationship between serum nutritional factors and bone mineral density: A Mendelian randomization study. J Clin Endocrinol Metab. 2021;106:e2434–e43.

    Article  PubMed  Google Scholar 

  56. Sun JY, Zhang H, Zhang Y, Wang L, Sun BL, Gao F, et al. Impact of serum calcium levels on total body bone mineral density: A mendelian randomization study in five age strata. Clin Nutr. 2021;40:2726–33.

    Article  CAS  PubMed  Google Scholar 

  57. O’Seaghdha CM, Wu H, Yang Q, Kapur K, Guessous I, Zuber AM, et al. Meta-analysis of genome-wide association studies identifies six new Loci for serum calcium concentrations. PLoS Genet. 2013;9:e1003796.

    Article  PubMed  PubMed Central  Google Scholar 

  58. de Lange KM, Moutsianas L, Lee JC, Lamb CA, Luo Y, Kennedy NA, et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat Genet. 2017;49:256–61.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Emdin CA, Khera AV, Natarajan P, Klarin D, Zekavat SM, Hsiao AJ, et al. Genetic association of Waist-to-Hip Ratio With Cardiometabolic Traits, Type 2 Diabetes, and coronary heart disease. JAMA. 2017;317:626–34.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Larsson SC, Burgess S, Michaelsson K. Association of genetic variants related to serum calcium levels with coronary artery disease and myocardial infarction. JAMA. 2017;318:371–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Burgess S, Gill D. Genetic evidence for vitamin D and cardiovascular disease: Choice of variants is critical. Eur Heart J. 2022;43:1740–2.

    Article  PubMed  Google Scholar 

  62. Yuan S, Xiong Y, Larsson SC. An atlas on risk factors for multiple sclerosis: a Mendelian randomization study. J Neurol. 2021;268:114–24.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank International Multiple Sclerosis Genetics Consortium (IMSGC) for the GWAS summary statistics.

Funding

This work was supported by funding from the National Natural Science Foundation of China (Grant No. 82071212, and 81901181), Beijing Natural Science Foundation (Grant No. JQ21022), the Mathematical Tianyuan Fund of the National Natural Science Foundation of China (Grant No. 12026414), and Beijing Ten Thousand Talents Project (Grant No. 2020A15). This work was also partially supported by funding from the Science and Technology Beijing One Hundred Leading Talent Training Project (Z141107001514006), the Beijing Municipal Administration of Hospitals’ Mission Plan (SML20150802), the Funds of Academic Promotion Programme of Shandong First Medical University & Shandong Academy of Medical Sciences (No. 2019QL016, No. 2019PT007).

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GYL and YZ conceived and initiated the project. GYL and YZ analyzed the data, and wrote the first draft of the manuscript. All authors contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript.

Corresponding author

Correspondence to Guiyou Liu.

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The authors declare no competing interests.

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This article contains human participants collected by several GWAS. All participants gave informed consent in all the corresponding original studies. Here, we only used the large-scale GWAS summary datasets, and not the individual-level data. Hence, ethical approval was not sought.

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Zhang, Y., Liu, H., Zhang, H. et al. Causal association of genetically determined circulating vitamin D metabolites and calcium with multiple sclerosis in participants of European descent. Eur J Clin Nutr 77, 481–489 (2023). https://doi.org/10.1038/s41430-023-01260-4

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