Rheumatoid arthritis and the risk of major cardiometabolic diseases: a Mendelian randomization study

Abstract

Objective

Rheumatoid arthritis (RA) is suggested to be implicated in the development of cardiometabolic diseases. We conducted a Mendelian randomization (MR) study to assess potential causality for associations of RA with the risk of cardiometabolic diseases, including type 2 diabetes (T2D), coronary artery disease (CAD), and ischaemic stroke.

Method

Seventy independent single-nucleotide polymorphisms (SNPs) associated with RA were identified as instrumental variables from a genome-wide association study (GWAS) of 58 284 European subjects. Summary-level data for the associations of the 70 genetic variants with T2D, CAD, and ischaemic stroke were taken from three GWASs with a total of 1 529 131 participants. Inverse-variance weighted (IVW) MR was used in the main analyses.

Results

The main IVW MR analysis showed that genetically determined RA was associated with higher risks of T2D [odds ratio (OR): 1.04, 95% confidence interval (CI) 1.02–1.05; p < 0.001] and CAD (OR: 1.02, 95% CI 1.00–1.03; p = 0.012), but not ischaemic stroke (OR: 1.00, 95% CI 0.99–1.02; p = 0.961). Sensitivity analyses with multiple MR methods confirmed these associations. MR-Egger regression showed no evidence of pleiotropy in the association between genetically determined RA and the risk of T2D, CAD, and ischaemic stroke. Leave-one-out sensitivity analysis showed that the association between genetically determined RA and the risk of T2D, CAD, and ischaemic stroke was not driven by any individual SNP.

Conclusion

Genetically determined RA was associated with increased risks of T2D and CAD, suggesting that RA plays a crucial role in the pathogenesis of T2D and CAD.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/03009742.2022.2070988.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China [grant numbers 82103917 and 82020108028]; the High Level Personnel Project of Jiangsu Province [grant number JSSCBS20210712]; the Natural Science Research Project of Jiangsu Provincial Higher Education [grant number 21KJB330006]; the Startup Fund from Soochow University [grant number Q413900420]; and a Project of the Priority Academic Program Development of Jiangsu Higher Education Institutions, China.