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ABSTRACT
This study aimed to elucidate the causal genetic relationships between iron, copper, zinc, magnesium, and rheumatoid arthritis (RA). A two-sample Mendelian randomization (MR) analysis was conducted using the “TwoSampleMR” and “MendelianRandomization” packages in R. The random-effects inverse variance-weighted (IVW) method was used as the primary approach. We performed sensitivity analyses to test the reliability of the results. The random-effects IVW analysis revealed that there was no genetic causal relationship between iron (P = 0.429, odds ratio [OR] 95% confidence interval [CI] = 0.919 [0.746–1.133]), copper (P = 0.313, OR 95% CI = 0.973 [0.921–1.027]), zinc (P = 0.633, OR 95% CI = 0.978 [0.891–1.073]), or magnesium (P = 0.218, OR 95% CI = 0.792 [0.546–1.148]) and RA. Sensitivity analysis verified the reliability of the results. Therefore, there is no evidence to support a causal relationship between iron, copper, zinc, and magnesium intake at the genetic level and the development of RA.
List of abbreviations
| RA | = | rheumatoid arthritis |
| MR | = | Mendelian randomization |
| SNPs | = | single nucleotide polymorphisms |
| IVs | = | instrumental variables |
| GWAS | = | genome-wide association studies |
| LD | = | linkage disequilibrium |
| IVW | = | inverse variance weighted |
| MR-PRESSO | = | MR pleiotropy residual sum and outlier |
| MR-RAPS | = | MR robust adjusted profile score |
| OR | = | odds ratio |
| CI | = | confidence interval |
| FHC1 | = | ferritin heavy chain 1 |
| FLS | = | fibroblast-like synoviocytes |
| IL | = | interleukin |
| TCA | = | tricarboxylic acid |
| TNF | = | tumor necrosis factor |
| NF-Κb | = | nuclear factor κB |
| FBS | = | fasting blood sugar |
| HOMA-IR | = | Homeostasis Model Assessment of Insulin Resistance. |
Disclosure statement
No potential conflict of interest was reported by the authors.
Authors’ contributions
The study was designed by Peng Xu and Mingyi Yang. Mingyi Yang, Yani Su, and Ke Xu conducted dataset analysis and interpreted the findings. Mingyi Yang, Xianjie Wan and Jiale Xie were responsible for data download. Lin Liu and Zhi Yang provided software support. The manuscript was written and edited by Mingyi Yang, with support from Peng Xu.
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/09603123.2023.2274377
Consent for publication
All authors agree to publish the manuscript in this journal.
Data availability statement
This study analyzed publicly available datasets that can be accessed via the FinnGen consortium (https://www.finngen.fi/) and the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/).