Abstract
Non-alcoholic fatty liver disease (NAFLD) has been found to be associated with osteoporosis (OP) in observational studies. However, the precise causal relationship between NAFLD and OP remains unclear. Here, we used Mendelian randomization (MR) to explore the causal relationship. We selected NAFLD-related single-nucleotide polymorphisms from a genome-wide meta-analysis (8434 cases and 434,770 controls) as instrumental variants. We used inverse variance weighted analysis for the primary MR analysis. Furthermore, we used similar methodologies in parallel investigations of other chronic liver diseases (CLDs). We performed sensitivity analyses to ensure the reliability of the results. We observed a causality between NAFLD and forearm bone mineral density (FABMD) (beta-estimate [β]: − 0.212; p-value: 0.034). We also found that sclerostin can act as a mediator to influence the NAFLD and FABMD pathways to form a mediated MR network (mediated proportion = 8.8%). We also identified indications of causal relationships between other CLDs and OP. However, we were unable to establish any associated mediators. Notably, our analyses did not yield any evidence of pleiotropy. Our findings have implications in the development of preventive and interventional measures aimed at managing low bone mineral density in patients with NAFLD.
Similar content being viewed by others
Data Availability
All genome-wide association studies can be accessed via the Open GWAS database (https://gwas.mrcieu.ac.uk/ and https://www.ebi.ac.uk/gwas/). The summary statistics of GWAS meta-analyses generated in this study can be accessible upon request.
References
Wang X, Malhi H (2018) Nonalcoholic fatty liver disease. Ann Intern Med 169:ITC65–ITC80. https://doi.org/10.7326/aitc201811060
Estes C, Razavi H, Loomba R, Younossi Z, Sanyal A (2018) Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease. Hepatology (Baltimore, Md.) 67:123–133. https://doi.org/10.1002/hep.29466
Byrne C, Targher G (2015) NAFLD: a multisystem disease. J Hepatol 62:S47-64. https://doi.org/10.1016/j.jhep.2014.12.012
Jeong H, Kim D (2019) Bone diseases in patients with chronic liver disease. Int J Mol Sci 20:4270. https://doi.org/10.3390/ijms20174270
Yang Y, Kim D (2021) An overview of the molecular mechanisms contributing to musculoskeletal disorders in chronic liver disease: osteoporosis, sarcopenia, and osteoporotic sarcopenia. Int J Mol Sci 22:2604. https://doi.org/10.3390/ijms22052604
Guañabens N, Parés A (2018) Osteoporosis in chronic liver disease. Liver Int 38:776–785. https://doi.org/10.1111/liv.13730
Upala S, Jaruvongvanich V, Wijarnpreecha K, Sanguankeo A (2017) Nonalcoholic fatty liver disease and osteoporosis: a systematic review and meta-analysis. J. Bone Miner Metab 35:685–693. https://doi.org/10.1007/s00774-016-0807-2
Bhatt S, Nigam P, Misra A, Guleria R, Qadar Pasha M (2013) Independent associations of low 25 hydroxy vitamin D and high parathyroid hormonal levels with nonalcoholic fatty liver disease in Asian Indians residing in north India. Atherosclerosis 230:157–163. https://doi.org/10.1016/j.atherosclerosis.2013.07.006
Burgess ST (2021) Mendelian Randomization: methods for causal inference using genetic variants, 2nd edn. Chapman and Hall/CRC, Boca Raton, p 224
He J, Huang M, Li N, Zha L, Yuan J (2023) Genetic association and potential mediators between sarcopenia and coronary heart disease: a bidirectional two-sample two-step Mendelian randomization study. Nutrients. https://doi.org/10.3390/nu15133013
Filip R, Radzki R, Bieńko M (2018) Novel insights into the relationship between nonalcoholic fatty liver disease and osteoporosis. Clin Interv Aging 13:1879–1891. https://doi.org/10.2147/cia.S170533
Danford C, Trivedi H, Papamichael K, Tapper E, Bonder A (2018) Osteoporosis in primary biliary cholangitis. World J Gastroenterol 24:3513–3520. https://doi.org/10.3748/wjg.v24.i31.3513
Verbanck M, Chen C, Neale B, Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50:693–698. https://doi.org/10.1038/s41588-018-0099-7
Yao S, Zhang M, Dong S, Wang J, Zhang K, Guo J, Guo Y, Yang T (2022) Bidirectional two-sample Mendelian randomization analysis identifies causal associations between relative carbohydrate intake and depression. Nat Hum Behav 6:1569–1576. https://doi.org/10.1038/s41562-022-01412-9
Ghodsian N, Abner E, Emdin C, Gobeil É, Taba N, Haas M, Perrot N, Manikpurage H, Gagnon É, Bourgault J et al (2021) Electronic health record-based genome-wide meta-analysis provides insights on the genetic architecture of non-alcoholic fatty liver disease. Cell reports. Medicine 2:100437. https://doi.org/10.1016/j.xcrm.2021.100437
Nuti R, Brandi M, Checchia G, Di Munno O, Dominguez L, Falaschi P, Fiore C, Iolascon G, Maggi S, Michieli R et al (2019) Guidelines for the management of osteoporosis and fragility fractures. Intern Emerg Med 14:85–102. https://doi.org/10.1007/s11739-018-1874-2
Medina-Gomez C, Kemp J, Trajanoska K, Luan J, Chesi A, Ahluwalia T, Mook-Kanamori D, Ham A, Hartwig F, Evans D et al (2018) Life-course genome-wide association study meta-analysis of total body BMD and assessment of age-specific effects. Am J Hum Genet 102:88–102. https://doi.org/10.1016/j.ajhg.2017.12.005
Wang L, Zhang C, Liang H, Zhou N, Huang T, Zhao Z, Luo X (2022) Polyunsaturated fatty acids level and bone mineral density: a two-sample Mendelian randomization study. Front Endocrinol (Lausanne) 13:858851. https://doi.org/10.3389/fendo.2022.858851
Zheng H, Forgetta V, Hsu Y, Estrada K, Rosello-Diez A, Leo P, Dahia C, Park-Min K, Tobias J, Kooperberg C et al (2015) Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 526:112–117. https://doi.org/10.1038/nature14878
Emdin C, Khera A, Kathiresan S (2017) Mendelian randomization. JAMA 318:1925–1926. https://doi.org/10.1001/jama.2017.17219
Liu K, Zou J, Fan H, Hu H, You Z (2022) Causal effects of gut microbiota on diabetic retinopathy: a Mendelian randomization study. Front Immunol 13:930318. https://doi.org/10.3389/fimmu.2022.930318
Lawlor D, Harbord R, Sterne J, Timpson N, Davey Smith G (2008) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27:1133–1163. https://doi.org/10.1002/sim.3034
Kamat M, Blackshaw J, Young R, Surendran P, Burgess S, Danesh J, Butterworth A, Staley J (2019) PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics (Oxford, England) 35:4851–4853. https://doi.org/10.1093/bioinformatics/btz469
Lee C, Cook S, Lee J, Han B (2016) Comparison of two meta-analysis methods: inverse-variance-weighted average and weighted sum of Z-scores. Genomics Inform 14:173–180. https://doi.org/10.5808/gi.2016.14.4.173
Wu H, Wang H, Liu D, Liu Z, Zhang W (2023) Mendelian randomization analyses of associations between breast cancer and bone mineral density. Sci Rep 13:1721. https://doi.org/10.1038/s41598-023-28899-0
Burgess S, Thompson S (2017) Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32:377–389. https://doi.org/10.1007/s10654-017-0255-x
Bowden J, Davey Smith G, Haycock P, Burgess S (2016) Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 40:304–314. https://doi.org/10.1002/gepi.21965
Milligan B (2003) Maximum-likelihood estimation of relatedness. Genetics 163:1153–1167. https://doi.org/10.1093/genetics/163.3.1153
Lawlor D, Tilling K, Davey Smith G (2016) Triangulation in aetiological epidemiology. Int J Epidemiol 45:1866–1886. https://doi.org/10.1093/ije/dyw314
Burgess S, Butterworth A, Thompson S (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37:658–665. https://doi.org/10.1002/gepi.21758
Solovieff N, Cotsapas C, Lee P, Purcell S, Smoller J (2013) Pleiotropy in complex traits: challenges and strategies. Nature reviews. Genetics 14:483–495. https://doi.org/10.1038/nrg3461
Mantovani A, Sani E, Fassio A, Colecchia A, Viapiana O, Gatti D, Idolazzi L, Rossini M, Salvagno G, Lippi G et al (2019) Association between non-alcoholic fatty liver disease and bone turnover biomarkers in post-menopausal women with type 2 diabetes. Diabetes Metab 45:347–355. https://doi.org/10.1016/j.diabet.2018.10.001
Carter A, Sanderson E, Hammerton G, Richmond R, Davey Smith G, Heron J, Taylor A, Davies N, Howe L (2021) Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol 36:465–478. https://doi.org/10.1007/s10654-021-00757-1
Kachuri L, Saarela O, Bojesen S, Davey Smith G, Liu G, Landi M, Caporaso N, Christiani D, Johansson M, Panico S et al (2019) Mendelian randomization and mediation analysis of leukocyte telomere length and risk of lung and head and neck cancers. Int J Epidemiol 48:751–766. https://doi.org/10.1093/ije/dyy140
Zhai T, Chen Q, Xu J, Jia X, Xia P (2021) Prevalence and trends in low bone density, osteopenia and osteoporosis in U.S. adults with non-alcoholic fatty liver disease, 2005–2014. Front Endocrinol (Lausanne) 12:825448. https://doi.org/10.3389/fendo.2021.825448
Pan B, Cai J, Zhao P, Liu J, Fu S, Jing G, Niu Q, Li Q (2022) Relationship between prevalence and risk of osteoporosis or osteoporotic fracture with non-alcoholic fatty liver disease: a systematic review and meta-analysis. Osteoporos Int 33:2275–2286. https://doi.org/10.1007/s00198-022-06459-y
Xie R, Liu M (2022) Relationship between non-alcoholic fatty liver disease and degree of hepatic steatosis and bone mineral density. Front Endocrinol (Lausanne) 13:857110. https://doi.org/10.3389/fendo.2022.857110
Mantovani A, Dauriz M, Gatti D, Viapiana O, Zoppini G, Lippi G, Byrne C, Bonnet F, Bonora E, Targher G (2019) Systematic review with meta-analysis: non-alcoholic fatty liver disease is associated with a history of osteoporotic fractures but not with low bone mineral density. Aliment Pharmacol Ther 49:375–388. https://doi.org/10.1111/apt.15087
Burgess S, Scott R, Timpson N, Davey Smith G, Thompson S (2015) Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol 30:543–552. https://doi.org/10.1007/s10654-015-0011-z
Bang CS, Shin IS, Lee SW, Kim JB, Baik GH, Suk KT, Yoon JH, Kim YS, Kim DJ (2015) Osteoporosis and bone fractures in alcoholic liver disease: a meta-analysis. World J Gastroenterol 21:4038–4047. https://doi.org/10.3748/wjg.v21.i13.4038
Ionele CM, Turcu-Stiolica A, Subtirelu MS, Ungureanu BS, Cioroianu GO, Rogoveanu I (2022) A systematic review and meta-analysis on metabolic bone disease in patients with primary sclerosing cholangitis. J Clin Med. https://doi.org/10.3390/jcm11133807
Saeki C, Saito M, Oikawa T, Nakano M, Torisu Y, Saruta M, Tsubota A (2020) Effects of denosumab treatment in chronic liver disease patients with osteoporosis. World J Gastroenterol 26:4960–4971. https://doi.org/10.3748/wjg.v26.i33.4960
Yu Y, Wang L, Ni S, Li D, Liu J, Chu H, Zhang N, Sun M, Li N, Ren Q et al (2022) Targeting loop3 of sclerostin preserves its cardiovascular protective action and promotes bone formation. Nat Commun 13:4241. https://doi.org/10.1038/s41467-022-31997-8
Marini F, Giusti F, Palmini G, Brandi M (2023) Role of Wnt signaling and sclerostin in bone and as therapeutic targets in skeletal disorders. Osteoporos Int 34:213–238. https://doi.org/10.1007/s00198-022-06523-7
Lee K, Lee J, Kim H, Yeom S, Woo C, Jung Y, Yun Y, Park S, Han J, Kim E et al (2021) Extracellular vesicles from adipose tissue-derived stem cells alleviate osteoporosis through osteoprotegerin and miR-21-5p. J Extracell Vesicles 10:e12152. https://doi.org/10.1002/jev2.12152
Fischer V, Haffner-Luntzer M (2022) Interaction between bone and immune cells: implications for postmenopausal osteoporosis. Semin Cell Dev Biol 123:14–21. https://doi.org/10.1016/j.semcdb.2021.05.014
Tella S, Gallagher J (2014) Prevention and treatment of postmenopausal osteoporosis. J Steroid Biochem Mol Biol 142:155–170. https://doi.org/10.1016/j.jsbmb.2013.09.008
Li S, Jiang H, Du N (2017) Association between osteoprotegerin gene T950C polymorphism and osteoporosis risk in the Chinese population: evidence via meta-analysis. PLoS ONE 12:e0189825. https://doi.org/10.1371/journal.pone.0189825
Oh H, Park S, Cho W, Abd El-Aty A, Hacimuftuoglu A, Kwon C, Jeong J, Jung T (2022) Sclerostin aggravates insulin signaling in skeletal muscle and hepatic steatosis via upregulation of ER stress by mTOR-mediated inhibition of autophagy under hyperlipidemic conditions. J Cell Physiol 237:4226–4237. https://doi.org/10.1002/jcp.30873
Albillos A, Lario M, Álvarez-Mon M (2014) Cirrhosis-associated immune dysfunction: distinctive features and clinical relevance. J Hepatol 61:1385–1396. https://doi.org/10.1016/j.jhep.2014.08.010
Zhou F, Wang Y, Li Y, Tang M, Wan S, Tian H, Chen X (2021) Decreased sclerostin secretion in humans and mice with nonalcoholic fatty liver disease. Front Endocrinol (Lausanne) 12:707505. https://doi.org/10.3389/fendo.2021.707505
Compston J (2018) Glucocorticoid-induced osteoporosis: an update. Endocrine 61:7–16. https://doi.org/10.1007/s12020-018-1588-2
Singh S, Dutta S, Khasbage S, Kumar T, Sachin J, Sharma J, Varthya S (2022) A systematic review and meta-analysis of efficacy and safety of romosozumab in postmenopausal osteoporosis. Osteoporos Int 33:1–12. https://doi.org/10.1007/s00198-021-06095-y
Acknowledgements
We extend our gratitude to the participants and investigators involved in the UK Biobank, FinnGen, GEFOS, eMERGE, NHSBT, KORA, YFS, and MAGIC research projects. In addition, we express appreciation towards all the other investigators for their efforts in providing accessible summary statistics.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
Conceptualization: Liu Y, Leng AM proposed the idea and designed the study; Data curation: Liu Yuan, and Cai LJ managed the data; Formal analysis: Liu Yuan, Meng QY, and He J analyzed the data; Funding acquisition: Leng AM acquire the funding; Investigation: Liu Yuan, Meng QY, and He J performed the research and collected data; Methodology: Liu Yuan and He J provided methodology; Project administration: Leng AM administrated the project; Resources: Liu Yuan, Meng QY, and He J coordinated the resources; Software: Liu Yuan, Meng QY, and He J developed the algorithm; Supervision: Leng AM supervised the study; Roles/Writing - original draft: Liu Y; Writing—review & editing: Leng AM.
Corresponding author
Ethics declarations
Conflict of interest
Yuan Liu, Mengqin Yuan, Jian He, Longjiao Cai, and Aimin Leng declare that they have no conflict of interest. The authors have no financial or proprietary interests in any material discussed in this article. The authors have no personal, financial, or institutional interest and ethical/legal conflicts involved in this article.
Ethical Approval
This study used only publicly available data so that no additional ethical approval was required. Details of ethical approval and participant consent for each study that contributed to GWAS can be found in the original publication.
Consent for Publication
Each author has given his/her consent to publication.
Statement of Human and Animal Rights
All data were derived from published studies or publicly available GWAS abstract data in which ethical approval and informed consent were provided. No separate ethical approval was required for this study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liu, Y., Yuan, M., He, J. et al. The Impact of Non-alcohol Fatty Liver Disease on Bone Mineral Density is Mediated by Sclerostin by Mendelian Randomization Study. Calcif Tissue Int 114, 502–512 (2024). https://doi.org/10.1007/s00223-024-01204-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00223-024-01204-5