Background Current knowledge on iron's role in rheumatoid arthritis (RA) development is very limited, with studies yielding inconsistent findings. We conducted a two-sample Mendelian randomization study to assess the associations of iron status with the risk of RA.
Methods This study leveraged genetic data from a large genome-wide association study (GWAS) of 257,953 individuals to identify single nucleotide polymorphisms (SNPs) associated with iron status. We then analyzed these data in conjunction with summary-level data on RA from the IEU open GWAS project, which included 5,427 RA cases and 479,171 controls. An inverse-variance weighted method with random effects was employed, along with sensitivity analyses, to assess the relationship between iron status and RA risk.
Results Genetic predisposition to high ferritin and serum iron status was causally associated with lower odds of RA. Ferritin had an odds ratio (OR) of 0.997 (95% confidence interval [CI]: 0.995–0.997; p = 0.010), indicating that a one-unit increase in ferritin is associated with a 0.3% decrease in the odds of RA. Similarly, serum iron had an OR of 0.997 (95% CI: 0.995–0.999; p = 0.014). However, MR analyses found no significant causal associations between total iron-binding capacity (OR = 1.0, 95% CI: 0.999–1.002; p = 0.592) or transferrin saturation percentage (OR = 0.998, 95% CI: 0.996–1.000; p = 0.080) and risk of developing RA.
Conclusions This study suggests that individuals with genes linked to higher iron levels may have a lower risk of developing RA. Our findings indicate that the total amount of iron in the body, rather than how it is distributed, might be more important for RA. This raises the intriguing possibility that iron supplementation could be a preventative strategy, but further research is necessary.
Keywords iron - ferritin - rheumatoid arthritis - Mendelian randomization - single nucleotide polymorphism Disclosure StatementNo potential conflict of interest was reported by the author.
This study analyzed publicly available datasets that can be accessed via NTNU Open Research Data (https://doi.org/10.18710/S9TJEL) and the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/).
Publication HistoryArticle published online:
29 August 2024
© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)
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