Mendelian randomization studies do not support the causal relationships between iron status and Intervertebral disc degeneration

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Introduction Context:Intervertebral disc degeneration (IVDD) is an important contributor of low back pain, which represents one of the most disabling symptoms within the adult population.Recently, increasing evidence suggests the potential association between iron status and IVDD. However, the causal relationship between these two common diseases remains unclear.We investigated the causal effects of four iron metabolism markers, regular iron supplementation and IVDD. 2. Methods: We conducted a two-sample Mendelian randomization (MR) analysis to assess the causal association between iron status and IVDD[1]. Sensitivity analysis was performed to test for heterogeneity and horizontal pleiotropy. 3. Results The genetically instrumented iron (odds ratio [OR]: 1.03; 95% confidence interval [CI]: 0.97–1.11; P=0.27)； ferritin(OR: 1.17; 95% CI: 0.99–1.38; P=0.07)； Liver iron content (OR: 1.04; 95% CI: 0.98–1.11; P=0.22)；Tranferrein(OR: 0.99; 95% CI: 0.91–1.08; P=0.85)；Tranferrein stautas (OR:1.02; 95% CI: 0.98–1.08; P=0.34)or supplement iron(OR:0.91; 95% CI: 0.79–1.05; P=0.18) showed no causal relationships with IVDD.No pleiotropic bias was found in the MR analyses. As heterogeneity was significant, a random model was used to minimize the effect of heterogeneity. 4. Conclusions No causal associations existed between iron status and IVDD. iron status and IVDD may represent separate entities. Intervertebral disc degeneration iron Mendelian randomization Figures Figure 1 Figure 2 Introduction Intervertebral disc degeneration (IVDD) is currently a common degraded condition in an aging society, referring to an age-dependent, cell-mediated molecular process[2.3]Degenerated discs are more prone to out-pouching and may press against the nerve roots, which eventually causes low back pain or other clinical symptoms. As an increasingly prevalent health problem, IVDD significantly impacts patients’ quality of life and poses a substantial economic burden to countries with rapidly aging populations[4.5.6].To date, in spite of the high prevalence of IVDD, lines of evidence for the risk factors of IVDD have not been fully established yet. Some observational studies have reported that the incidence of IVDD and in assemia patients is much greater than in the general[2.7.8]Some observational studies have reported iron metabolism dysfunction in IVDD cases, suggesting a positive association between serum ferritin,and IVDD[ 7 ].In contrast, some observational studies have found that serum iron is not associated with IVDD[ 9 ].The related pathological changes that cause IVDD include extracellular matrix degradation, apoptosis, senescence, and inflammation[ 10 ].The high incidence of IVDD in patients with iron overload has led to increased interest in the association between iron status and IVDD.Iron, which is involved in many critical biological activities, such as oxygen transport, DNA biosynthesis, and ATP creation, is a trace element vital to cell function[ 11 ].Iron metabolism is divided into four components: uptake, storage, utilization, and export[ 12 ].By studying the signal transduction pathway related to iron status, oxidative stress and lipid metabolism, it can be concluded that iron status is related to IVDD[13.14.15.16.17]. However, the causal relationship between iron status and IVDD is not clear. It is noteworthy that these observational studies were limited in making causal inferences because of potential biases introduced by confounders and reverse causality. Currently, Mendelian randomization (MR) analysis is increasingly used to estimate causal inferences between exposures and outcomes. MR analysis resembles the random assignment of participants to treatment and control groups in a randomized controlled trial because the genetic variants are randomly assorted during gamete formation, which can minimize the effect of confounders and reverse causality. In this study,So far, we are the first study to infer a causality between iron status and IVDD using In two sample MR.The evidence would provide crucial information on the causal relationship between iron status and IVDD.We utilized genome-wide association study (GWAS) databases to conduct a two-sample MR analysis to elucidate the causal effects among four iron metabolism markers (serum iron, ferritin, transferrin and liver iron levels), regular iron supplementation, and risk of IVDD. Study Design The single-nucleotide polymorphisms (SNPs) identified as genetic variants had to meet the following three assumptions[Fig. 1 ]: (1) SNPs were strongly associated with exposures; (2) SNPs were not related to any confounders of the exposure–outcome associations; and (3) SNPs only affected outcomes via exposures. Ethics approval was not applicable to these analyses because all included genome-wide association studies (GWAS) data were publicly available and had been approved by the corresponding ethical review board. Data Source Data on iron metabolism were based on a GWAS consisting of 23,986 European individuals from the Integrative Epidemiology Unit (IEU) open GWAS project( https://gwas.mrcieu.ac.uk/)an d included information regarding serum iron, log10 ferritin, transferrin[ 18 ]. The summary-level GWAS data for the liver iron level was from the IEU open GWAS project.including 32,858 European ancestry individuals .The summary-level GWAS data of iron supplements were obtained from the UK Biobank mineral and other dietary supplements, 13,865 cases and 440,960 controls were included in this study[ 19 ]. The summary results for Intervertebral Disc Degeneration (IVDD) were acquired from the FinnGen consortium, specifically from the R10 release (Data download - FinnGen Public Documentation (gitbook.io)). This dataset encompasses 41,669 cases and 294,700 controls. IVDD diagnoses were based on the International Classification of Diseases, specifically ICD-10 (M51), ICD-9 722, and ICD-8 725 coding standard.[ 20 ] All study analyses are based on publicly available GWAS aggregate statistics ( http://gwas.mrcieu.au.uk ) and do not require additional ethical approval or informed consent. Selection and Validation of SNPs First, our SNP selection criteria for (iron status, IVDD) : associated with a genome significance threshold exposure (P<5×10 − 8).For iron-supplemented SNPS, we selected the ones associated with genomic exposure as (P 0.001 and clumping window <10,000 kb). Third, the F statistic was calculated to verify the strength of the SNP, deleting SNPs with an F statistic less than 10. The data were harmonized to ensure that SNP effects on exposure and outcome corresponded to the same allele. MR Analyses The inverse-variance weighted (IVW) metaanalysis under a random-effect model was utilized as the principal analysis. The following five methods, including weighted median, MR-Egger, multivariable MR, simple mode, and weighted mode, were also performed to ensure the robustness of the analyses. The weighted median method can provide valid estimates even if up to 50% of information comes from invalid genetic variants[ 21 ]. The MR-Egger method can assess and adjust the effect of horizontal pleiotropy of selected genetic variants[ 22 ]. Funnel plots can also detect horizontal pleiotropy if asymmetry exists. Multivariable MR analyses were performed by considering the body mass index (BMI) as a potential confounder or intermediator. Furthermore, a leaveone-out sensitivity analysis can analyze the influence of an individual SNP on the overall estimates. Cochrane’s Q value can assess heterogeneity among selected genetic variants. All statistical analyses were performed by utilizing the “TwoSampleMR” package in R software ( "R version 4.3.1 (2023-06-16 ucrt)"). Results All relevant SNPS and sources are summarized in Table 1 . According to the IVW method, the genetically instrumented iron (odds ratio [OR]: 1.03; 95% confidence interval [CI]: 0.97–1.11; P = 0.27); ferritin(OR: 1.17; 95% CI: 0.99–1.38; P = 0.07)༛ Liver iron content (OR: 1.04; 95% CI: 0.98–1.11; P = 0.22)༛Tranferrein(OR: 0.99; 95% CI: 0.91–1.08; P = 0.85)༛or supplement iron(OR:0.91; 95% CI: 0.79–1.05; P = 0.18)showed no relationships with IVDD.In addition to the first, we used four additional methods (MR-Egger,Weighted median,Simple mode and Weighted mode), all of which proved no association with IVDD[Fig. 2 ]. Table 1 :All SNP related data and sources Id Population Source N case P Ferritin European Open Gwas 23,986 <5×10 − 8 Iron European Open Gwas 23,986 <5×10 − 8 Transferrin European Open Gwas 23,986 <5×10 − 8 Liver Iron European Open Gwas 32,858 <5×10 − 8 Supplement Iron European Gwas Catalog 454,825 <5×10 − 6 IVDD European FINN 336,436 <5×10 − 8 IVW under a random model was applied to minimize the effect of heterogeneity.The IVW method revealed no evidence of horizontal pleiotropy.Meanwhile,The heterogeneity was irrelevant in all analyses[Table 2 ].Other relevant data analysis diagrams will be placed in Supplement1. Table 2 Interrelated horizontal pleiotropy and heterogeneity Analysis Q(P) Horizontal Pleiotropy Ferritin on IVDD 2.98(0.22) 0.45 Iron on IVDD 0.02(0.99) 0.94 Liver iron content on IVDD 0.56(0.76) 0.6 Supplement iron on IVDD 0.51(0.77) 0.64 Transferrin on IVDD 6.12(0.11) 0.41 Discussion Using genetic variants associated with iron status and IVDD, our two-sample MR analyses showed that iron status had no causal relationships with IVDD. Although it is generally believed that iron status and intervertebral disc degeneration disorders are separate entities,several studies have suggested an association between iron and IVDD[ 23 ]. Concordant with this assumption, an overview of trials (217 patients) of IVDD suggested serum ferritin was negatively correlated with the degree of IvDD(r = − 0.185, p = 0.006)[ 7 ].Instantly, A test(69 patients)shows that Serum iron was not associated with the degree of intervertebral disc degeneration(r = 0.045 p = 0.711)[ 8 ];.It is not possible to infer a causality whether iron status or IVDD because potential biases introduced by confounders and reverse causality.In terms of genes and signaling pathways multiple signalling pathways have been involved in IVDD. Nuclear factor Kappa β, Toll-Like receptors, YAP1, and mitogen-activated protein kinase pathways mediate proinfammatory genes, such as Iron Ions Chelation, GPX4 and IL-6 et al, which are related to the development and progression of IVDD[13.14.15.16.17].These signal transduction pathways related to iron status are involved in oxidative stress, cartilage growth, etc., and have become a hot topic in recent research.For these signal transduction pathways, the position of chromosomes may affect the expression of genes, and the expression of some genes is an important part of the signal transduction pathway. Therefore, we can say that signal transduction pathways and chromosome location are related to a certain extent.In general, in the field of signal transduction pathway research, they do not consider the interaction between multiple pathways.As genetic variants are randomly allocated before birth, we can minimize the effect of confounders and reverse causation on outcomes by using genetic variants strongly associated with exposure as instrumental variables.Due to the interaction of multiple signal transduction pathways, there are many correlations with genes themselves, so it is impossible to completely judge whether causality exists. Our selected database, divided by disease and blood phenotype, can replace this judgment and evaluate causality. However,several limitations deserve our attention. First,the results of the current MR analyses may not be generalizable to non-European populations, given that most GWAS primarily enrolled European individuals.Besides,our database of iron status, the sample size is too small and the total number of SNPS is not enough. This leads to a lack of statistical strength for the data selection of IVDD. Conclusion Our comprehensively two-sample MR analyse suggested no causal associations between iron status and IVDD. This finding confirms that iron stauts and IVDD represent separate entities. Consequently, treatment strategies for IVDD are mainly limited to pain relief or surgical treatment.The results of previous studies may be influenced by the presence of confounding factors and signal transduction pathway interactions. Declarations Funding Funded by Shaanxi provincial key research and development project Nature Fund Conflict of interest The author declares no conflict of interest Acknowledgement The authors express their gratitude to the participants and investigators of FinnGen consortium ,IEU OpenGwas and Gwas Catalog for sharing the GWAS summary data. References Davey Smith G, Hemani G (2014) Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 23:R89-98. R. Haidar, K.M. Musallam, A.T. Taher, Bone disease and skeletal complications in patients with beta thalassemia major, Bone 48 (2011) 425–432 Clinical studies have reported that the incidence of lower back pain and scoliosis in thalassemia patients is much greater than in the general. Fenn J, Olby NJ. Canine spinal cord injury consortium (CANSORT-SCI). classification of intervertebral disc disease. Front Vet Sci (2020) 7:579025. doi: 10.3389/fvets.2020.579025 P.P. Vergroesen, I. Kingma, K.S. Emanuel, R.J. Hoogendoorn, T.J. Welting, B.J. van Royen, J.H. van Die¨ en, T.H. Smit, Mechanics and biology in intervertebral disc Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the global burden of disease study 2017. Lancet (2019) 394(10204):1145–58. doi: 10.1016/S0140- 6736(19)30427-1 4. Wu D, Wong P, Guo C, Tam LS, Gu J. Pattern and trend of five major musculoskeletal disorders in China from 1990 to 2017: findings from the global burden of disease study 2017. BMC Med (2021) 19(1):34. doi: 10.1186/s12916-021- 01905-w Guo Y, Li C, Shen B, Chen X, Hu T, Wu D. Is intervertebral disc degeneration associated with reduction in serum ferritin?. Eur Spine J . 2022;31(11):2950-2959. doi:10.1007/s00586-022-07361-1 Chang H, Xu J, Li X, Zhao R, Wang M, Ding W. Association between anemia and lumbar disc degeneration in patients with low back pain: an observational retrospective study. Eur Spine J . 2023;32(6):2059-2068. doi:10.1007/s00586-023-07652-1 Zhao B, Wang K, Zhao J, Luo Y. Serum calcium concentration as an indicator of intervertebral disk degeneration prognosis. Biol Trace Elem Res . 2013;154(3):333-337. doi:10.1007/s12011-013-9747-z Cazzanelli, P., and Wuertz-Kozak, K. (2020). MicroRNAs in intervertebral disc degeneration, apoptosis, inflammation, and mechanobiology. Int. J. Mol. Sci. 21 (10), 3601. doi:10.3390/ijms21103601 Bogdan, A. R., Miyazawa, M., Hashimoto, K., and Tsuji, Y. (2016). Regulators of iron homeostasis: New players in metabolism, cell death, and disease. Trends Biochem. Sci. 41 (3), 274–286. doi:10.1016/j.tibs.2015.11.012 Chen, X., Li, J., Kang, R., Klionsky, D. J., and Tang, D. (2021). Ferroptosis: Machinery and regulation. Autophagy 17 (9), 2054–2081. doi:10.1080/15548627.2020.1810918 Bin S, Xin L, Lin Z, Jinhua Z, Rui G, Xiang Z. Targeting miR-10a-5p/IL-6R axis for reducing IL-6-induced cartilage cell ferroptosis. Exp Mol Pathol . 2021;118:104570. doi:10.1016/j.yexmp.2020.104570 Dong W, Liu J, Lv Y, Wang F, Liu T, Sun S et al (2019) miR-640 aggravates intervertebral disc degeneration via NF-κB and WNT signalling pathway. Cell Prolif 52(5):e12664. https://doi.org/10. 1111/cpr.12664 Fang F, Jiang D (2016) IL-1β/HMGB1 signalling promotes the infammatory cytokines release via TLR signalling in human intervertebral disc cells. Biosci Rep. https://doi.org/10.1042/ BSR20160118 Wang J, Hu J, Chen X, Huang C, Lin J, Shao Z et al (2019) BRD4 inhibition regulates MAPK, NF-κB signals, and autophagy to suppress MMP-13 expression in diabetic intervertebral disc degeneration. Faseb J 33(10):11555–11566. https://doi.org/10.1096/f. 201900703R Zhou LP, Zhang RJ, Jia CY, et al. Ferroptosis: A potential target for the intervention of intervertebral disc degeneration. Front Endocrinol (Lausanne) . 2022;13:1042060. Published 2022 Oct 20. doi:10.3389/fendo.2022.1042060 Benyamin B, Esko T, Ried JS, et al. Novel loci affecting iron homeostasis and their effects in individuals at risk for hemochromatosis [published correction appears in Nat Commun. 2015;6:6542. Häldin, Jonas [corrected to Hälldin, Jonas]]. Nat Commun . 2014;5:4926. Published 2014 Oct 29. doi:10.1038/ncomms5926 Jiang L, Zheng Z, Fang H, Yang J. A generalized linear mixed model association tool for biobank-scale data. Nat Genet . 2021;53(11):1616-1621. doi:10.1038/s41588-021-00954-4 FinnGen provides genetic insights from a well-phenotyped isolated population Burgess S, Bowden J, Fall T, Ingelsson E and Thompson SG: Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology, 2017; 28: 30-42 Bowden J, Davey Smith G and Burgess S: Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol, 2015; 44: 512-525 Yang X, Chen Y, Guo J, et al. Polydopamine Nanoparticles Targeting Ferroptosis Mitigate Intervertebral Disc Degeneration Via Reactive Oxygen Species Depletion, Iron Ions Chelation, and GPX4 Ubiquitination Suppression. Adv Sci (Weinh) . 2023;10(13):e2207216. doi:10.1002/advs.202207216 Chang H, Xu J, Li X, Zhao R, Wang M, Ding W. Association between anemia and lumbar disc degeneration in patients with low back pain: an observational retrospective study. Eur Spine J . 2023;32(6):2059-2068. doi:10.1007/s00586-023-07652-1 Additional Declarations No competing interests reported. Supplementary Files supplementall.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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As an increasingly prevalent health problem, IVDD significantly impacts patients’ quality of life and poses a substantial economic burden to countries with rapidly aging populations[4.5.6].To date, in spite of the high prevalence of IVDD, lines of evidence for the risk factors of IVDD have not been fully established yet. Some observational studies have reported that the incidence of IVDD and in assemia patients is much greater than in the general[2.7.8]Some observational studies have reported iron metabolism dysfunction in IVDD cases, suggesting a positive association between serum ferritin,and IVDD[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e].In contrast, some observational studies have found that serum iron is not associated with IVDD[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e].The related pathological changes that cause IVDD include extracellular matrix degradation, apoptosis, senescence, and inflammation[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e].The high incidence of IVDD in patients with iron overload has led to increased interest in the association between iron status and IVDD.Iron, which is involved in many critical biological activities, such as oxygen transport, DNA biosynthesis, and ATP creation, is a trace element vital to cell function[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].Iron metabolism is divided into four components: uptake, storage, utilization, and export[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].By studying the signal transduction pathway related to iron status, oxidative stress and lipid metabolism, it can be concluded that iron status is related to IVDD[13.14.15.16.17].\u003c/p\u003e\n\u003cp\u003eHowever, the causal relationship between iron status and IVDD is not clear.\u003c/p\u003e\n\u003cp\u003eIt is noteworthy that these observational studies were limited in making causal inferences because of potential biases introduced by confounders and reverse causality. Currently, Mendelian randomization (MR) analysis is increasingly used to estimate causal inferences between exposures and outcomes. MR analysis resembles the random assignment of participants to treatment and control groups in a randomized controlled trial because the genetic variants are randomly assorted during gamete formation, which can minimize the effect of confounders and reverse causality. In this study,So far, we are the first study to infer a causality between iron status and IVDD using In two sample MR.The evidence would provide crucial information on the causal relationship between iron status and IVDD.We utilized genome-wide association study (GWAS) databases to conduct a two-sample\u003c/p\u003e\n\u003cp\u003eMR analysis to elucidate the causal effects among four iron metabolism markers (serum iron, ferritin, transferrin and liver iron levels), regular iron supplementation, and risk of IVDD.\u003c/p\u003e\n\n\n\n\n\n\n\n\n\n"},{"header":"Study Design","content":"\u003cp\u003eThe single-nucleotide polymorphisms (SNPs) identified as genetic variants had to meet the following three assumptions[Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e]: (1) SNPs were strongly associated with exposures; (2) SNPs were not related to any confounders of the exposure\u0026ndash;outcome associations; and (3) SNPs only affected outcomes via exposures. Ethics approval was not applicable to these analyses because all included genome-wide association studies (GWAS) data were publicly available and had been approved by the corresponding ethical review board.\u003c/p\u003e\n\u003cp\u003eData Source\u003c/p\u003e\n\u003cp\u003eData on iron metabolism were based on a GWAS consisting of 23,986 European individuals from the Integrative Epidemiology Unit (IEU) open GWAS project(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/)an\u003c/span\u003e\u003c/span\u003ed included information regarding serum iron, log10 ferritin, transferrin[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. The summary-level GWAS data for the liver iron level was from the IEU open GWAS project.including 32,858 European ancestry individuals .The summary-level GWAS data of iron supplements were obtained from the UK Biobank mineral and other dietary supplements, 13,865 cases and 440,960 controls were included in this study[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe summary results for Intervertebral Disc Degeneration (IVDD) were acquired from the FinnGen consortium, specifically from the R10 release (Data download - FinnGen Public Documentation (gitbook.io)). This dataset encompasses 41,669 cases and 294,700 controls. IVDD diagnoses were based on the International Classification of Diseases, specifically ICD-10 (M51), ICD-9 722, and ICD-8 725 coding standard.[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eAll study analyses are based on publicly available GWAS aggregate statistics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gwas.mrcieu.au.uk\u003c/span\u003e\u003c/span\u003e) and do not require additional ethical approval or informed consent.\u003c/p\u003e\n\u003cp\u003eSelection and Validation of SNPs\u003c/p\u003e\n\u003cp\u003eFirst, our SNP selection criteria for (iron status, IVDD) : associated with a genome significance threshold exposure (P\u0026lt;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8).For iron-supplemented SNPS, we selected the ones associated with genomic exposure as (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;6). Second, the independence of the selected SNPs was evaluated using the pairwise-linkage disequilibrium[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e],excluding the SNPs in linkage disequilibrium (r 2\u0026gt; 0.001 and clumping window \u0026lt;10,000 kb). Third, the F statistic was calculated to verify the strength of the SNP, deleting SNPs with an F statistic less than 10. The data were harmonized to ensure that SNP effects on exposure and outcome corresponded to the same allele.\u003c/p\u003e\n\u003cp\u003eMR Analyses\u003c/p\u003e\n\u003cp\u003eThe inverse-variance weighted (IVW) metaanalysis under a random-effect model was utilized as the principal analysis. The following five methods, including weighted median, MR-Egger, multivariable MR, simple mode, and weighted mode, were also performed to ensure the robustness of the analyses. The weighted median method can provide valid estimates even if up to 50% of information comes from invalid genetic variants[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. The MR-Egger method can assess and adjust the effect of horizontal pleiotropy of selected genetic variants[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Funnel plots can also detect horizontal pleiotropy if asymmetry exists. Multivariable MR analyses were performed by considering the body mass index (BMI) as a potential confounder or intermediator. Furthermore, a leaveone-out sensitivity analysis can analyze the influence of an individual SNP on the overall estimates. Cochrane\u0026rsquo;s Q value can assess heterogeneity among selected genetic variants. All statistical analyses were performed by utilizing the \u0026ldquo;TwoSampleMR\u0026rdquo; package in R software ( \u0026quot;R version 4.3.1 (2023-06-16 ucrt)\u0026quot;).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAll relevant SNPS and sources are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. According to the IVW method, the genetically instrumented iron (odds ratio [OR]: 1.03; 95% confidence interval [CI]: 0.97\u0026ndash;1.11; P\u0026thinsp;=\u0026thinsp;0.27); ferritin(OR: 1.17; 95% CI: 0.99\u0026ndash;1.38; P\u0026thinsp;=\u0026thinsp;0.07)༛ Liver iron content (OR: 1.04; 95% CI: 0.98\u0026ndash;1.11; P\u0026thinsp;=\u0026thinsp;0.22)༛Tranferrein(OR: 0.99; 95% CI: 0.91\u0026ndash;1.08; P\u0026thinsp;=\u0026thinsp;0.85)༛or supplement iron(OR:0.91; 95% CI: 0.79\u0026ndash;1.05; P\u0026thinsp;=\u0026thinsp;0.18)showed no relationships with IVDD.In addition to the first, we used four additional methods (MR-Egger,Weighted median,Simple mode and Weighted mode), all of which proved no association with IVDD[Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e]. \u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e:All SNP related data and sources\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eId\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003ePopulation\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eSource\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eN case\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eP\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003eFerritin\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eEuropean\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eOpen Gwas\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e23,986\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003eIron\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eEuropean\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eOpen Gwas\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e23,986\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003eTransferrin\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eEuropean\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eOpen Gwas\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e23,986\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003eLiver Iron\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eEuropean\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eOpen Gwas\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e32,858\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003eSupplement Iron\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eEuropean\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGwas Catalog\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e454,825\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;6\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003eIVDD\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eEuropean\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eFINN\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e336,436\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eIVW under a random model was applied to minimize the effect of heterogeneity.The IVW method revealed no evidence of horizontal pleiotropy.Meanwhile,The heterogeneity was irrelevant in all analyses[Table\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e].Other relevant data analysis diagrams will be placed in Supplement1. \u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInterrelated horizontal pleiotropy and heterogeneity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eAnalysis\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eQ(P)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eHorizontal Pleiotropy\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eFerritin on IVDD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e2.98(0.22)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.45\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIron on IVDD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.02(0.99)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.94\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eLiver iron content on IVDD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.56(0.76)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.6\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSupplement iron on IVDD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.51(0.77)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.64\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTransferrin on IVDD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e6.12(0.11)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.41\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eUsing genetic variants associated with iron status and IVDD, our two-sample MR analyses showed that iron status had no causal relationships with IVDD. Although it is generally believed that iron status and intervertebral disc degeneration disorders are separate entities,several studies have suggested an association between iron and IVDD[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eConcordant with this assumption, an overview of trials (217 patients) of IVDD suggested serum ferritin was negatively correlated with the degree of IvDD(r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.185, p\u0026thinsp;=\u0026thinsp;0.006)[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e].Instantly, A test(69 patients)shows that Serum iron was not associated with the degree of intervertebral disc degeneration(r\u0026thinsp;=\u0026thinsp;0.045 p\u0026thinsp;=\u0026thinsp;0.711)[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e];.It is not possible to infer a causality whether iron status or IVDD because potential biases introduced by confounders and reverse causality.In terms of genes and signaling pathways multiple signalling pathways have been involved in IVDD. Nuclear factor Kappa \u0026beta;, Toll-Like receptors, YAP1, and mitogen-activated protein kinase pathways mediate proinfammatory genes, such as Iron Ions Chelation, GPX4 and IL-6 et al, which are related to the development and progression of IVDD[13.14.15.16.17].These signal transduction pathways related to iron status are involved in oxidative stress, cartilage growth, etc., and have become a hot topic in recent research.For these signal transduction pathways, the position of chromosomes may affect the expression of genes, and the expression of some genes is an important part of the signal transduction pathway. Therefore, we can say that signal transduction pathways and chromosome location are related to a certain extent.In general, in the field of signal transduction pathway research, they do not consider the interaction between multiple pathways.As genetic variants are randomly allocated before birth, we can minimize the effect of confounders and reverse causation on outcomes by using genetic variants strongly associated with exposure as instrumental variables.Due to the interaction of multiple signal transduction pathways, there are many correlations with genes themselves, so it is impossible to completely judge whether causality exists. Our selected database, divided by disease and blood phenotype, can replace this judgment and evaluate causality. However,several limitations deserve our attention. First,the results of the current MR analyses may not be generalizable to non-European populations, given that most GWAS primarily enrolled European individuals.Besides,our database of iron status, the sample size is too small and the total number of SNPS is not enough. This leads to a lack of statistical strength for the data selection of IVDD.\u003c/p\u003e\n\n"},{"header":"Conclusion","content":"\u003cp\u003eOur comprehensively two-sample MR analyse suggested no causal associations between iron status and IVDD. This finding confirms that iron stauts and IVDD represent separate entities. Consequently, treatment strategies for IVDD are mainly limited to pain relief or surgical treatment.The results of previous studies may be influenced by the presence of confounding factors and signal transduction pathway interactions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunded by Shaanxi provincial key research and development project Nature Fund\u003c/p\u003e\n\u003cp\u003eConflict of interest The author declares no conflict of interest\u003c/p\u003e\n\u003cp\u003eAcknowledgement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the participants and investigators\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eof \u0026nbsp;FinnGen consortium ,IEU OpenGwas and Gwas Catalog for sharing the GWAS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003esummary data.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDavey Smith G, Hemani G (2014) Mendelian randomization: genetic anchors for causal inference in epidemiological studies. 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Polydopamine Nanoparticles Targeting Ferroptosis Mitigate Intervertebral Disc Degeneration Via Reactive Oxygen Species Depletion, Iron Ions Chelation, and GPX4 Ubiquitination Suppression. \u003cem\u003eAdv Sci (Weinh)\u003c/em\u003e. 2023;10(13):e2207216. doi:10.1002/advs.202207216\u003c/li\u003e\n\u003cli\u003eChang H, Xu J, Li X, Zhao R, Wang M, Ding W. Association between anemia and lumbar disc degeneration in patients with low back pain: an observational retrospective study. \u003cem\u003eEur Spine J\u003c/em\u003e. 2023;32(6):2059-2068. doi:10.1007/s00586-023-07652-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Intervertebral disc degeneration, iron, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4136489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4136489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e1. Introduction\u003c/p\u003e\n\u003cp\u003eContext:Intervertebral disc degeneration (IVDD) is an important contributor of low back pain, which represents one of the most disabling symptoms within the adult population.Recently, increasing evidence suggests the potential association between iron status and IVDD. However, the causal relationship between these two common diseases remains unclear.We investigated the causal effects of four iron metabolism markers, regular iron supplementation and IVDD.\u003c/p\u003e\n\u003cp\u003e2. Methods:\u003c/p\u003e\n\u003cp\u003eWe conducted a two-sample Mendelian randomization (MR) analysis to assess the causal association between iron status and IVDD[1]. Sensitivity analysis was performed to test for heterogeneity and horizontal pleiotropy.\u003c/p\u003e\n\u003cp\u003e3. Results\u003c/p\u003e\n\u003cp\u003eThe genetically instrumented iron (odds ratio [OR]: 1.03; 95% confidence interval [CI]: 0.97–1.11; P=0.27)； ferritin(OR: 1.17; 95% CI: 0.99–1.38; P=0.07)； Liver iron content (OR: 1.04; 95% CI: 0.98–1.11; P=0.22)；Tranferrein(OR: 0.99; 95% CI: 0.91–1.08; P=0.85)；Tranferrein stautas (OR:1.02; 95% CI: 0.98–1.08; P=0.34)or supplement iron(OR:0.91; 95% CI: 0.79–1.05; P=0.18) showed no causal relationships with IVDD.No pleiotropic bias was found in the MR analyses. As heterogeneity was significant, a random model was used to minimize the effect of heterogeneity.\u003c/p\u003e\n\u003cp\u003e4. Conclusions\u003c/p\u003e\n\u003cp\u003eNo causal associations existed between iron status and IVDD. iron status and IVDD may represent separate entities.\u003c/p\u003e","manuscriptTitle":"Mendelian randomization studies do not support the causal relationships between iron status and Intervertebral disc degeneration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-25 05:46:22","doi":"10.21203/rs.3.rs-4136489/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"229199b8-c1b5-4ca0-974f-4d79ba06bd9e","owner":[],"postedDate":"March 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-05T08:27:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-25 05:46:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4136489","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4136489","identity":"rs-4136489","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}