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/)and 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 < 5×10 − 6). Second, the independence of the selected SNPs was evaluated using the pairwise-linkage disequilibrium[19],excluding the SNPs in linkage disequilibrium (r 2> 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)").